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measeval | Data were drawn from the Whitehall II study with baseline examination in 1991; follow-up screenings in 1997, 2003, and 2008; and additional disease ascertainment from hospital data and registry linkage on 5318 participants (mean age 54.8 years, 31% women) without depressive symptoms at baseline. Vascular risk was assessed with the Framingham Cardiovascular, Coronary Heart Disease, and Stroke Risk Scores. New depressive symptoms at each follow-up screening were identified by General Health Questionnaire caseness, a Center for Epidemiologic Studies Depression Scale score ≥16, and use of antidepressant medication. | train | S0006322312001096-1136 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "baseline examination",
"measured_property": null,
"quantity": "1991",
"unit": null
},
{
"measured_entity": "follow-up screenings",
"measured_property": null,
"quantity": "1997, 2003, and 2008",
"unit": null
},
{
"measured_entity": "Whitehall II study",
"measured_property": null,
"quantity": "5318 participants",
"unit": "participants"
},
{
"measured_entity": "5318 participants",
"measured_property": "mean age",
"quantity": "54.8 years",
"unit": "years"
},
{
"measured_entity": "5318 participants",
"measured_property": "women",
"quantity": "31%",
"unit": "%"
},
{
"measured_entity": "Center for Epidemiologic Studies Depression Scale",
"measured_property": "score",
"quantity": "≥16",
"unit": null
}
] |
measeval | The Whitehall II study is a prospective cohort study of British civil servants established in 1985 to study associations between risk factors, pathophysiological changes, and clinical disease (22). The target population was all London-based office staff, aged 35 to 55, working in 20 civil service departments on recruitment to the study in 1985 to 1988 (phase 1). With a response of 73%, the cohort consisted of 10,308 employees (6895 men and 3413 women). Since the phase 1 medical examination, follow-up examinations have taken place approximately every 5 years: phase 3 (1991 to 1993, n = 8815); phase 5 (1997 to 1999, n = 7870); phase 7 (2003 to 2004, n = 6967); and phase 9 (2008 to 2009, n = 6761). Phase 3 was the first time that all components of the Framingham risk algorithms were measured, making it the baseline for the analyses we report here. We use data from three screening cycles: from phase 3 to phase 5, from phase 5 to phase 7, and from phase 7 to phase 9 with phases 3, 5, and 7 providing baseline measures of vascular risk to assess incidence of depressive symptoms at phases 5, 7, and 9 in the three data cycles, respectively. | train | S0006322312001096-1177 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "Whitehall II study",
"measured_property": "established",
"quantity": "1985",
"unit": null
},
{
"measured_entity": "target population",
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"quantity": "35 to 55",
"unit": null
},
{
"measured_entity": "civil service departments",
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"unit": null
},
{
"measured_entity": "target population",
"measured_property": "recruitment",
"quantity": "1985 to 1988",
"unit": null
},
{
"measured_entity": "cohort",
"measured_property": "response",
"quantity": "73%",
"unit": null
},
{
"measured_entity": "cohort",
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"quantity": "10,308 employees",
"unit": "employees"
},
{
"measured_entity": "cohort",
"measured_property": null,
"quantity": "6895 men",
"unit": "men"
},
{
"measured_entity": "cohort",
"measured_property": null,
"quantity": "3413 women",
"unit": "women"
},
{
"measured_entity": "follow-up examinations",
"measured_property": "taken place",
"quantity": "approximately every 5 years",
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},
{
"measured_entity": "phase 3",
"measured_property": null,
"quantity": "1991 to 1993",
"unit": null
},
{
"measured_entity": "phase 3",
"measured_property": "n",
"quantity": "8815",
"unit": null
},
{
"measured_entity": "phase 5",
"measured_property": null,
"quantity": "1997 to 1999",
"unit": null
},
{
"measured_entity": "phase 5",
"measured_property": "n",
"quantity": "7870",
"unit": null
},
{
"measured_entity": "phase 7",
"measured_property": null,
"quantity": "2003 to 2004",
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},
{
"measured_entity": "phase 7",
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"quantity": "6967",
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},
{
"measured_entity": "phase 9",
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"quantity": "2008 to 2009",
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},
{
"measured_entity": "phase 9",
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},
{
"measured_entity": "screening cycles",
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"quantity": "three",
"unit": null
},
{
"measured_entity": "data cycles",
"measured_property": null,
"quantity": "three",
"unit": null
}
] |
measeval | We used standard operating protocols to measure risk factors for the Framingham general CVD risk score (sex, age, diabetes, smoking, treated and untreated systolic blood pressure, total cholesterol, high-density lipoprotein [HDL] cholesterol), CHD risk score (sex, age, diabetes, smoking, systolic and diastolic blood pressure, total cholesterol, HDL cholesterol), and stroke risk score (sex, age, systolic blood pressure, diabetes, smoking, prior cardiovascular disease, atrial fibrillation, left-ventricular hypertrophy, use of hypertensive medications) at the baseline of each data cycle, i.e., phases 3, 5, and 7 (14–17). Venous blood was taken in the fasting state or at least 5 hours after a light, fat-free breakfast. Serum for lipid analyses was refrigerated at −4°C and assayed within 72 hours. Cholesterol was measured with the use of a Cobas Fara centrifugal analyzer (Roche Diagnostics System, Nutley, New Jersey). High-density lipoprotein cholesterol was measured by precipitating non-HDL cholesterol with dextran sulfate-magnesium chloride using a centrifuge and measuring cholesterol in the supernatant. Participants underwent an oral glucose tolerance test and new venous blood samples were taken at 2 hours postadministration of a 75 g glucose solution. Blood glucose was measured using the glucose oxidase method on a YSI MODEL 2300 STAT PLUS Analyzer (YSI Corporation, Yellow Springs, Ohio; mean coefficient of variation: 1.4%–3.1%). Diabetes was defined by fasting glucose ≥7.0 mmol/L or 2-hour postload glucose ≥11.1 mmol/L, reported doctor diagnosed diabetes, or use of diabetes medication (24). We measured systolic and diastolic blood pressure twice in the sitting position after 5 minutes rest with a Hawksley random-zero sphygmomanometer (phases 3 and 5) (Lynjay Services Ltd., Worthing, United Kingdom) and OMRON HEM 907 (phase 7) (Omron, Milton Keynes, United Kingdom), the average of two readings used in the analysis. Atrial fibrillation was identified on the Glasgow 12-lead electrocardiogram analysis program combined with manual review (Prof. P. Macfarlane, University of Glasgow, United Kingdom). Limb lead electrocardiograms were classified according to the Minnesota code (25), with tall R waves (codes 3-1 to 3-3) used to reflect left ventricular hypertrophy. Information on smoking and use of antihypertensive drugs and lipid-lowering medication was requested at phases 3, 5, and 7. | train | S0006322312001096-1190 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "Venous blood",
"measured_property": "taken",
"quantity": "at least 5 hours",
"unit": "hours"
},
{
"measured_entity": "Serum for lipid analyses",
"measured_property": "refrigerated",
"quantity": "−4°C",
"unit": "°C"
},
{
"measured_entity": "Serum for lipid analyses",
"measured_property": "assayed",
"quantity": "within 72 hours",
"unit": "hours"
},
{
"measured_entity": "new venous blood samples",
"measured_property": "taken",
"quantity": "2 hours",
"unit": "hours"
},
{
"measured_entity": "glucose solution",
"measured_property": null,
"quantity": "75 g",
"unit": "g"
},
{
"measured_entity": "glucose oxidase method",
"measured_property": "mean coefficient of variation",
"quantity": "1.4%–3.1%",
"unit": "%"
},
{
"measured_entity": "fasting glucose",
"measured_property": null,
"quantity": "≥7.0 mmol/L",
"unit": "mmol/L"
},
{
"measured_entity": "postload glucose",
"measured_property": null,
"quantity": "2-hour",
"unit": "hour"
},
{
"measured_entity": "2-hour postload glucose",
"measured_property": null,
"quantity": "≥11.1 mmol/L",
"unit": "mmol/L"
},
{
"measured_entity": "rest",
"measured_property": null,
"quantity": "5 minutes",
"unit": "minutes"
}
] |
measeval | The validity of the Framingham risk scores as measures of vascular risk was supported in our study, as they strongly predicted incidence of subsequent (manifest) vascular disease. Age- and sex-adjusted odds ratio for 10% increment in risk was 2.84 (95% confidence interval [CI] 1.66–4.88) for the stroke risk score-incident stroke association and 2.25 (95% CI 1.92–2.65) for the CHD risk score-incident CHD association. The corresponding odds ratios for the associations of CVD risk score with stroke and CHD were 1.34 (95% CI 1.11–1.62) and 2.14 (95% CI 1.85–2.48), respectively. | train | S0006322312001096-1194 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "risk",
"measured_property": "increment",
"quantity": "10%",
"unit": "%"
},
{
"measured_entity": "stroke risk score-incident stroke association",
"measured_property": "Age- and sex-adjusted odds ratio",
"quantity": "2.84",
"unit": null
},
{
"measured_entity": "confidence interval [CI]",
"measured_property": null,
"quantity": "95%",
"unit": "%"
},
{
"measured_entity": "stroke risk score-incident stroke association",
"measured_property": "Age- and sex-adjusted odds ratio",
"quantity": "1.66–4.88",
"unit": null
},
{
"measured_entity": "CHD risk score-incident CHD association",
"measured_property": "Age- and sex-adjusted odds ratio",
"quantity": "2.25",
"unit": null
},
{
"measured_entity": "CI",
"measured_property": null,
"quantity": "95%",
"unit": "%"
},
{
"measured_entity": "CHD risk score-incident CHD association",
"measured_property": "Age- and sex-adjusted odds ratio",
"quantity": "1.92–2.65",
"unit": null
},
{
"measured_entity": "CVD",
"measured_property": "odds ratios",
"quantity": "1.34",
"unit": null
},
{
"measured_entity": "CI",
"measured_property": null,
"quantity": "95%",
"unit": "%"
},
{
"measured_entity": "CVD",
"measured_property": "odds ratios",
"quantity": "1.11–1.62",
"unit": null
},
{
"measured_entity": "CHD",
"measured_property": "odds ratios",
"quantity": "2.14",
"unit": null
},
{
"measured_entity": "CI",
"measured_property": null,
"quantity": "95%",
"unit": "%"
},
{
"measured_entity": "CHD",
"measured_property": "odds ratios",
"quantity": "1.85–2.48",
"unit": null
}
] |
measeval | We used three indicators (or proxy measures) to identify persons with depressive symptoms. First, participants responded to the self-administered 30-item General Health Questionnaire (GHQ) at phases 3, 5, 7, and 9, a screening instrument designed for and widely used in population-based surveys and trials (26). Each questionnaire item enquires about a specific symptom, with response categories scored as either 1 or 0 to indicate whether the symptom is present. Total score of 5 or more led to individuals being defined as GHQ-symptom cases and scores 0 to 4 as noncases (27). Although the GHQ was originally designed to assess depressive and anxiety symptoms, a recent population-based study showed GHQ caseness to be sensitive (84%) and specific (84%) in detecting dysthymia or major depressive disorder, as indicated by the Composite International Diagnostic Interview (28). The GHQ has also been validated at baseline against a clinical interview schedule in the Whitehall II study, with acceptable sensitivity (73%) and specificity (78%) (27). In a more recent validation using a subgroup of 274 participants aged 58 to 70 in 2010, the sensitivity and specificity of GHQ symptom caseness against diagnosed depressive episodes based on a structured psychiatric interview were 80% and 81%, respectively. | train | S0006322312001096-1197 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "indicators (or proxy measures)",
"measured_property": null,
"quantity": "three",
"unit": null
},
{
"measured_entity": "General Health Questionnaire (GHQ)",
"measured_property": null,
"quantity": "30-item",
"unit": "item"
},
{
"measured_entity": "response categories",
"measured_property": "scored",
"quantity": "1 or 0",
"unit": null
},
{
"measured_entity": "GHQ-symptom cases",
"measured_property": "score",
"quantity": "5 or more",
"unit": null
},
{
"measured_entity": "noncases",
"measured_property": "scores",
"quantity": "0 to 4",
"unit": null
},
{
"measured_entity": "GHQ caseness",
"measured_property": "sensitive",
"quantity": "84%",
"unit": "%"
},
{
"measured_entity": "GHQ caseness",
"measured_property": "specific",
"quantity": "84%",
"unit": "%"
},
{
"measured_entity": "GHQ",
"measured_property": "sensitivity",
"quantity": "73%",
"unit": "%"
},
{
"measured_entity": "GHQ",
"measured_property": "specificity",
"quantity": "78%",
"unit": "%"
},
{
"measured_entity": "subgroup",
"measured_property": null,
"quantity": "274 participants",
"unit": "participants"
},
{
"measured_entity": "subgroup of 274 participants",
"measured_property": "aged",
"quantity": "58 to 70",
"unit": null
},
{
"measured_entity": "more recent validation",
"measured_property": null,
"quantity": "2010",
"unit": null
},
{
"measured_entity": "GHQ symptom caseness",
"measured_property": "sensitivity",
"quantity": "80%",
"unit": "%"
},
{
"measured_entity": "GHQ symptom caseness",
"measured_property": "specificity",
"quantity": "81%",
"unit": "%"
}
] |
measeval | Second, at phases 7 and 9, depressive symptoms were also assessed using the Center for Epidemiologic Studies Depression Scale (CES-D) (note that CES-D was not included in earlier screening phases of our study) (29). The 20 items of the CES-D measure symptoms associated with depression; participants are asked to score the frequency of occurrence of specific symptoms during the previous week on a four point scale (0 = less than one day, 1 = 1–2 days, 2 = 3–4 days, and 3 = 5–7 days). These items are summed to yield a total score between 0 and 60, with participants scoring ≥16 defined as having CES-D depressive symptoms (30). In a validation study of 274 Whitehall II participants, sensitivity and specificity were 89% and 86%, respectively, for CES-D depressive symptoms using a structured psychiatric interview as the criterion. | train | S0006322312001096-1202 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "CES-D",
"measured_property": null,
"quantity": "20 items",
"unit": "items"
},
{
"measured_entity": "scale",
"measured_property": null,
"quantity": "four point",
"unit": "point"
},
{
"measured_entity": "four point scale",
"measured_property": "0",
"quantity": "less than one day",
"unit": "day"
},
{
"measured_entity": "four point scale",
"measured_property": "1",
"quantity": "1–2 days",
"unit": "days"
},
{
"measured_entity": "four point scale",
"measured_property": "2",
"quantity": "3–4 days",
"unit": "days"
},
{
"measured_entity": "four point scale",
"measured_property": "3",
"quantity": "5–7 days",
"unit": "days"
},
{
"measured_entity": "total score",
"measured_property": null,
"quantity": "between 0 and 60",
"unit": null
},
{
"measured_entity": "defined as having CES-D depressive symptoms",
"measured_property": "scoring",
"quantity": "≥16",
"unit": null
},
{
"measured_entity": "validation study",
"measured_property": null,
"quantity": "274 Whitehall II participants",
"unit": "Whitehall II participants"
},
{
"measured_entity": "structured psychiatric interview",
"measured_property": "sensitivity",
"quantity": "89%",
"unit": "%"
},
{
"measured_entity": "structured psychiatric interview",
"measured_property": "specificity",
"quantity": "86%",
"unit": "%"
}
] |
measeval | Third, at phases 3, 5, 7, and 9, participants were asked whether they had taken any medication in the past 14 days and, if so, to provide the name of the medication. Medications were coded using British National Formulary codes to define antidepressant medication use (codes: 040301–040304) (31). | train | S0006322312001096-1205 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "participants",
"measured_property": "taken any medication",
"quantity": "past 14 days",
"unit": "days"
}
] |
measeval | We constructed Framingham CVD, CHD, and stroke risk scores for each participant and used logistic regression to examine the association of each risk score at baseline with depressive symptoms at follow-up. Crude, age- and sex-adjusted, and multivariably adjusted odds ratios and 95% confidence intervals per 10% absolute increase in risk score were calculated. In the analysis of GHQ symptoms, the sample was large enough to detect, with 90% power at a significance level of .05, an odds ratio of 1.14 for a 10% increase in the Framingham CVD risk score. For depressive symptoms defined by CES-D and antidepressant medication, the corresponding odds ratios were 1.40 and 1.24, respectively. | train | S0006322312001096-1221 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "confidence intervals",
"measured_property": null,
"quantity": "95%",
"unit": "%"
},
{
"measured_entity": "risk score",
"measured_property": "absolute increase",
"quantity": "10%",
"unit": "%"
},
{
"measured_entity": "power",
"measured_property": null,
"quantity": "90%",
"unit": "%"
},
{
"measured_entity": "significance level",
"measured_property": null,
"quantity": ".05",
"unit": null
},
{
"measured_entity": "10% increase in the Framingham CVD risk score",
"measured_property": "odds ratio",
"quantity": "1.14",
"unit": null
},
{
"measured_entity": "Framingham CVD risk score",
"measured_property": "increase",
"quantity": "10%",
"unit": "%"
},
{
"measured_entity": "depressive symptoms defined by CES-D",
"measured_property": "odds ratios",
"quantity": "1.40",
"unit": null
},
{
"measured_entity": "antidepressant medication",
"measured_property": "odds ratios",
"quantity": "1.24",
"unit": null
}
] |
measeval | Differences between the analytic sample and the excluded participants were generally small (Table S1 in Supplement 1). Compared with those included, participants excluded from the analyses were more likely to be women, nonwhite, and current smokers, although other characteristics were similar in the two groups. Across the 5-year data cycles, 12.1% of participants without GHQ symptoms developed such symptoms, 4.6% of those without CES-D depressive symptoms had such symptoms at follow-up, and 2.1% of those not on antidepressant treatment started such medication. Of the cases of GHQ symptoms, 5.4% used antidepressant medication, the corresponding proportion being 9.2% for those with CES-D depressive symptoms. Of participants with GHQ symptoms, 47.4% also had CES-D depressive symptoms (Pearson correlation at phase 7 r = .64, p < .001). Conversely, among participants with CES-D depressive symptoms, 59.9% also had GHQ symptoms. | train | S0006322312001096-1230 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "data cycles",
"measured_property": null,
"quantity": "5-year",
"unit": "year"
},
{
"measured_entity": "participants without GHQ symptoms",
"measured_property": "developed such symptoms",
"quantity": "12.1%",
"unit": "%"
},
{
"measured_entity": "those without CES-D depressive symptoms",
"measured_property": "had such symptoms at follow-up",
"quantity": "4.6%",
"unit": "%"
},
{
"measured_entity": "those not on antidepressant treatment",
"measured_property": "started such medication",
"quantity": "2.1%",
"unit": "%"
},
{
"measured_entity": "cases of GHQ symptoms",
"measured_property": "used antidepressant medication",
"quantity": "5.4%",
"unit": "%"
},
{
"measured_entity": "those with CES-D depressive symptoms",
"measured_property": "used antidepressant medication",
"quantity": "9.2%",
"unit": "%"
},
{
"measured_entity": "participants with GHQ symptoms,",
"measured_property": "also had CES-D depressive symptoms",
"quantity": "47.4%",
"unit": "%"
},
{
"measured_entity": "Pearson correlation at phase 7",
"measured_property": "r",
"quantity": ".64",
"unit": null
},
{
"measured_entity": "Pearson correlation at phase 7",
"measured_property": "p",
"quantity": "< .001",
"unit": null
},
{
"measured_entity": "participants with CES-D depressive symptoms",
"measured_property": "also had GHQ symptoms",
"quantity": "59.9%",
"unit": "%"
}
] |
measeval | After adjustment for multiple covariates (age, sex, marital status, education, socioeconomic status, retirement, cognitive impairment, body mass index, alcohol consumption, menopausal status [women], and nonvascular chronic condition), the association between vascular disease and subsequent CES-D depressive symptoms remained in participants with no prevalent or previous depressive episodes, as indicated by the Composite International Diagnostic Interview (Table 3). Higher stroke risk score was associated with higher odds of onset of CES-D depressive symptoms before (odds ratio 2.30, 95% CI 1.03–5.13), but not after, the age 65. The CVD and CHD risk scores were not associated with subsequent depressive symptoms. | train | S0006322312001096-1248 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "CI",
"measured_property": null,
"quantity": "95%",
"unit": "%"
}
] |
measeval | This longitudinal study of British adults shows that manifest cardiovascular disease (CHD and stroke) is associated with increased risk of depressive symptoms. There was also some evidence to suggest an association between higher Framingham stroke risk score and increased onset of depressive symptoms before the age of 65 years. However, the vascular risk scores did not predict later-life depressive symptoms among persons with no manifest vascular disease or history of depression. This finding was based on data from three different vascular risk prediction algorithms—the Framingham CVD, CHD, and stroke scores—and depressive symptoms measured using two survey instruments and information on use of antidepressant medication. | train | S0006322312001096-1253 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "increased onset of depressive symptoms",
"measured_property": "before the age",
"quantity": "65 years",
"unit": "years"
}
] |
measeval | A few previous studies have examined the longitudinal association between overall vascular risk and later-life depression using other indicators of vascular risk status. In the Health, Aging and Body Composition study of adults aged 70 or higher, a score combining selected vascular risk factors (body mass index, metabolic syndrome, smoking, and ankle arm index) and vascular diseases predicted higher 2-year incidence of elevated depressive symptoms (7). However, the extent to which this association was attributable to vascular risk factors alone is difficult to assess in such a study design. In the Rotterdam study of older adults followed up for 6 years (11), none of the objective atherosclerosis measures was linked with subsequent depression. This null finding was evident irrespective of whether assessment of depression was based on CES-D or a clinical diagnosis obtained from a psychiatric interview (11). Similarly, in the Leiden 85+ prospective cohort study, ratings of generalized atherosclerosis were not associated with depressive symptoms (43). | train | S0006322312001096-1260 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "adults",
"measured_property": "aged",
"quantity": "70 or highe",
"unit": null
},
{
"measured_entity": "elevated depressive symptoms",
"measured_property": "incidence",
"quantity": "2-year",
"unit": "year"
},
{
"measured_entity": "older adults",
"measured_property": "followed up",
"quantity": "for 6 years",
"unit": "years"
},
{
"measured_entity": "objective atherosclerosis measures",
"measured_property": null,
"quantity": "none",
"unit": null
}
] |
measeval | There are caveats to the results reported here. We measured depressive symptoms using validated instruments, such as the General Health Questionnaire and CES-D, and information on prescribed antidepressant medication use (26,29,49). These instruments are not designed to make a psychiatric diagnosis of first or recurrent major depression (26,29); they defined only partially overlapping case populations and some misclassification occurred because antidepressants are also prescribed for conditions other than depression. Nevertheless, the associations of manifest CHD and stroke with depressive symptoms in our study are in agreement with previous research linking vascular disease to depressive symptoms and clinical depression (4,35–38,44). Furthermore, in a validation study of 274 elderly participants from our cohort, sensitivity and specificity of the questionnaire measures with depression diagnosed based on structured psychiatric interview as the criterion are high, almost 90% for CES-D depressive symptoms and approximately 80% for GHQ symptoms. | train | S0006322312001096-1271 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "validation study",
"measured_property": null,
"quantity": "274 elderly participants",
"unit": "elderly participants"
},
{
"measured_entity": "the questionnaire measures",
"measured_property": "sensitivity and specificity",
"quantity": "almost 90%",
"unit": "%"
},
{
"measured_entity": "the questionnaire measures",
"measured_property": "sensitivity and specificity",
"quantity": "approximately 80%",
"unit": "%"
}
] |
measeval | Despite a high response to the survey (range 66% to 88%) at the successive data collection phases, loss to follow-up accumulated over the extended follow-up, as is inevitable in long-term prospective studies. However, differences between the included and excluded participants were generally small. Our study is based on an occupational cohort, which, by its very nature, is healthier than the general population, so the range of vascular risk scores and the range of depressive symptom measurements are likely to be narrower. This being the case, the associations between the Framingham risk scores and depressive symptoms reported here could underestimate the strength of associations in the general population, although similar associations between manifest vascular disease and depressive symptoms in the present dataset and those from the general population suggest that our estimates are likely to be fairly accurate. Due to the relatively low numbers of depressive symptom cases in the present data, we cannot detect weak associations between vascular risk scores and later-life depressive symptoms. | train | S0006322312001096-1275 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "survey",
"measured_property": "response",
"quantity": "range 66% to 88%",
"unit": "%"
}
] |
measeval | These results suggest that public health measures to improve vascular status will influence the incidence of later-life depression, primarily via reduced rates of manifest vascular disease. Our findings on diagnosed vascular disease support current clinical guidelines to screen patients with coronary heart disease or stroke for depressive symptoms. However, although the stroke risk score was associated with depressive symptoms before the age of 65, we found little evidence to suggest that standard clinical information on preclinical vascular risk status would be helpful in the prediction of depressive symptoms after the age of 65 years. Thus, extending screening of incident later-life depression to include those identified as having a high risk of developing vascular disease was not supported by this study. | train | S0006322312001096-1278 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "depressive symptoms",
"measured_property": "age",
"quantity": "65",
"unit": null
},
{
"measured_entity": "depressive symptoms",
"measured_property": null,
"quantity": "65 years",
"unit": "years"
}
] |
measeval | Association Between Framingham Risk Scores (per 10% Increase) and Subsequent Onset of Depressive Symptoms Before and After Age 65 | train | S0006322312001096-626 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "Framingham Risk Scores",
"measured_property": "Increase",
"quantity": "10%",
"unit": null
},
{
"measured_entity": "Depressive Symptoms",
"measured_property": "Age",
"quantity": "65",
"unit": null
}
] |
measeval | Environmental changes associated with the Paleocene–Eocene thermal maximum (PETM, ∼56 Ma) have not yet been documented in detail from the North Sea Basin. Located within proximity to the North Atlantic igneous province (NAIP), the Kilda Basin, and the northern rain belt (paleolatitude 54 °N) during the PETM, this is a critical region for testing proposed triggers of atmospheric carbon release that may have caused the global negative carbon isotope excursion (CIE) in marine and terrestrial environments. The CIE onset is identified from organic matter δ13C in exceptional detail within a highly expanded sedimentary sequence. Pollen and spore assemblages analysed in the same samples for the first time allow a reconstruction of possible changes to vegetation on the surrounding landmass. Multiproxy palynological, geochemical, and sedimentologic records demonstrate enhanced halocline stratification and terrigenous deposition well before (103 yrs) the CIE, interpreted as due to either tectonic uplift possibly from a nearby magmatic intrusion, or increased precipitation and fluvial runoff possibly from an enhanced hydrologic cycle. Stratification and terrigenous deposition increased further at the onset and within the earliest CIE which, coupled with evidence for sea level rise, may be interpreted as resulting from an increase in precipitation over NW Europe consistent with an enhanced hydrologic cycle in response to global warming during the PETM. Palynological evidence indicates a flora dominated by pollen from coastal swamp conifers before the CIE was abruptly replaced with a more diverse assemblage of generalist species including pollen similar to modern alder, fern, and fungal spores. This may have resulted from flooding of coastal areas due to relative sea level rise, and/or ecologic changes forced by climate. A shift towards more diverse angiosperm and pteridophyte vegetation within the early CIE, including pollen similar to modern hickory, documents a long term change to regional vegetation. | train | S0012821X12004384-1148 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "Paleocene–Eocene thermal maximum (PETM",
"measured_property": null,
"quantity": "∼56 Ma",
"unit": "Ma"
},
{
"measured_entity": "North Atlantic igneous province (NAIP), the Kilda Basin, and the northern rain belt",
"measured_property": "paleolatitude",
"quantity": "54 °N",
"unit": "°N"
},
{
"measured_entity": "halocline stratification and terrigenous deposition",
"measured_property": null,
"quantity": "before (103 yrs)",
"unit": "yrs"
}
] |
measeval | The PETM was a period of geologically-rapid global warming that punctuated a warming Eocene climate 55.8 Ma ago (Charles et al., 2011), and saw sea surface temperatures rise by 5–8 °C from background levels (Zachos et al., 2005; Sluijs et al., 2007). It was associated with a substantial injection of δ13C-depleted carbon into the ocean-atmosphere system (see Pagani et al., 2006a) over <20 ka (Cui et al., 2011), causing a negative carbon isotope excursion (CIE) between −2 and −7‰ in marine and terrestrial sediments (see overview in Schouten et al., 2007) lasting 170 ka (Röhl et al., 2007), and a prominent dissolution horizon in the deep sea signifying deep ocean acidification (Kennett and Stott 1991; Zachos et al., 2005). The source and rate of released carbon are still under debate (Pagani et al., 2006a; Zeebe et al., 2009; Cui et al., 2011), but may have been linked to the dissociation of marine hydrates containing biogenic methane (δ13C of<−60‰) (Dickens et al., 1995), thermogenic methane from marine sediments around the Norwegian Sea (Svensen et al., 2004), or dissolved methane from a silled Kilda Basin between Greenland and Norway (Nisbet et al., 2009). | train | S0012821X12004384-1178 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "PETM",
"measured_property": null,
"quantity": "55.8 Ma",
"unit": "Ma"
},
{
"measured_entity": "sea surface temperatures",
"measured_property": "rise",
"quantity": "5–8 °C",
"unit": "°C"
},
{
"measured_entity": "δ13C-depleted carbon",
"measured_property": "substantial injection",
"quantity": "over <20 ka",
"unit": "ka"
},
{
"measured_entity": "marine and terrestrial sediments",
"measured_property": "negative carbon isotope",
"quantity": "between −2 and −7‰",
"unit": "‰"
},
{
"measured_entity": "negative carbon isotope excursion (CIE)",
"measured_property": "lasting",
"quantity": "170 ka",
"unit": "ka"
},
{
"measured_entity": "marine hydrates containing biogenic methane",
"measured_property": "δ13C",
"quantity": "<−60‰",
"unit": "‰"
}
] |
measeval | During the late Paleocene–early Eocene the North Sea was a restricted marine basin, characterised by siliciclastic sedimentation and high terrigenous input, principally from the Scotland–Faeroe–Shetland landmass (Knox 1998, Fig. S1). Core 22/10a-4 is located in the central part of the basin (Figs. 1 and S1) and is therefore disconnected from many marginal marine processes that could mask oceanographic signals (e.g. tidal or storm-induced erosion and slumping). Paleobathymetry estimates in the North Sea during the Paleocene and Eocene are difficult to constrain accurately, as the extant benthic foraminifera present in the Paleogene are found today living between 200 and >1000 m water depth (Gradstein et al., 1992), and are controlled predominantly by substrate and bottom water properties. However using a number of paleoecologic micropaleontology methods together (Gillmore et al., 2001), along with 2D structural restoration (Kjennerud and Sylta, 2001), broad agreement was found and central parts of the northern North Sea appear to have had paleodepths of >0.5 km in the earliest Eocene near 22/10a-4 (Kjennerud and Gillmore, 2003, Fig. S1). | train | S0012821X12004384-1221 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "water",
"measured_property": "depth",
"quantity": "between 200 and >1000 m",
"unit": "m"
},
{
"measured_entity": "central parts of the northern North Sea",
"measured_property": "paleodepths",
"quantity": ">0.5 km",
"unit": "km"
}
] |
measeval | As 22/10a-4 is in the deep (>0.5 km) central part of the basin, it acted as a depocentre and exhibits a Paleocene-Eocene transition sequence that is not only expanded but is also close to being stratigraphically complete. The only evidence for breaks in the succession is minor erosion at the base of thin turbidite sandstones (typically <10 cm). Because these sandstones may contain reworked material, they were not sampled in this study. During the late Paleocene, the basin became restricted following a fall in in the order of 100 m that resulted from regional uplift associated with the proto-Iceland mantle plume in the North Atlantic (see Knox, 1996). This event is evident in 22/10a-4 as a lithologic change from unbedded to bedded mudstone (the Lista and Sele Formation boundary, Fig. 2). Restriction of the basin also led to the establishment of poorly oxygenated bottom waters, as is evident by a shift in the benthic foraminiferal assemblages towards low diversity low oxygen-tolerant agglutinated species (Knox, 1996). The CIE at the Paleocene–Eocene boundary was accompanied by a relative sea level rise, as documented in southeast England (Powell et al., 1996) and Spitsbergen (Harding et al., 2011), due to the thermal expansion of sea water and possible melting of ice caps, although the North Sea basin remained restricted as evidenced by the persistence of low oxygen facies in 22/10a-4. The North Sea had a widespread freshwater catchment area, and a halocline was in place from the late Paleocene to early Eocene (Zacke et al., 2009). Therefore, surface water salinity changes in the North Sea Basin provide a sensitive gauge for stratification forced by changes in tectonics and the hydrologic cycle. | train | S0012821X12004384-1232 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "central part of the basin",
"measured_property": "deep",
"quantity": ">0.5 km)",
"unit": "km"
},
{
"measured_entity": "turbidite sandstones",
"measured_property": "thin",
"quantity": "<10 cm",
"unit": "cm"
},
{
"measured_entity": "basin",
"measured_property": "fall in",
"quantity": "in the order of 100 m",
"unit": "m"
}
] |
measeval | Borehole 22/10a-4 (57°44’8.47”N; 1°50’26.59”E) provides a continuous core through the Forties Sandstone Member and into the Lista Formation (Fig. 2). The core consists of variably fissile claystone with interbedded fine to coarse grained sandstone layers interpreted as turbidites, with occasional mm-thick ash layers (Fig. 3). All samples in this study were taken from claystone horizons to avoid sampling substantial quantities of reworked material. The section of 22/10a-4 analysed in this study is from 2605 m to 2634 m (core depth), chosen because this part of the core is predominantly in claystone facies and provides a greatly expanded section over the onset of the CIE (Figs. 2 and 3). At 2609–2613 m the claystone becomes finely laminated with alternately pale and dark laminae couplets ranging from 1 to 25 per mm (Fig. S2). The pale laminae consist of clay and silt, and the dark laminae are rich in organic carbon and pyrite inclusions. Laminae were counted at 26 horizons throughout the core and approximately 13 pairs per mm. | train | S0012821X12004384-1249 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "Borehole 22/10a-4",
"measured_property": null,
"quantity": "57°44’8.47”N; 1°50’26.59”E",
"unit": null
},
{
"measured_entity": "section of 22/10a-4",
"measured_property": "core depth",
"quantity": "from 2605 m to 2634 m",
"unit": "m"
},
{
"measured_entity": "the claystone",
"measured_property": "becomes finely laminated",
"quantity": "2609–2613 m",
"unit": "m"
},
{
"measured_entity": "alternately pale and dark laminae couplets",
"measured_property": null,
"quantity": "ranging from 1 to 25 per mm",
"unit": "mm"
},
{
"measured_entity": "core",
"measured_property": null,
"quantity": "26 horizons",
"unit": "horizons"
},
{
"measured_entity": "Laminae",
"measured_property": null,
"quantity": "approximately 13 pairs per mm",
"unit": "pairs per mm"
}
] |
measeval | A total of 71 palynology samples were prepared at the British Geological Survey using standard preparation procedures (Moore et al., 1991). Samples were demineralised with hydrochloric (HCl) and hydrofluoric (HF) acids, and residual mineral grains removed using heavy liquid (zinc bromide) separation. Elvacite was used to mount slides. The palynomorphs were analysed using a Nikon transmitted light microscope, counting the total number of palynomorphs on a strew slide (Table S1). Each slide was produced from 1/100th of the total material processed, where the initial weight of material was 5 g of dried sediment. Thus, the palynology counts represent the total number of specimens per 0.05 g of dried sediment. Statistical analysis was carried out using the software of Hammer et al. (2005). The %wood/plant tissue was determined by palynological investigation, and is the sum of ‘%wood plant tissue’ and ‘%various (non-woody) plant tissue’ in Table S1. Organic material for δ13CAOM analysis was collected from the same palynology samples, and the remaining processed material separated into size fractions. The >250 μm fractions, found through light microscope analysis to be dominated (>90%) by amorphous organic matter (AOM), were also analysed for δ13C. Foraminifera samples between 20 and 60 g of dried sediment were processed by washing through a 63 μm sieve with water. All specimens were counted and converted to foraminifera/g (Table S2). All species exhibited agglutinated (non calcareous) test walls. | train | S0012821X12004384-1265 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "palynology samples",
"measured_property": null,
"quantity": "71",
"unit": null
},
{
"measured_entity": "total material processed",
"measured_property": "Each slide was produced",
"quantity": "1/100th",
"unit": null
},
{
"measured_entity": "dried sediment",
"measured_property": "initial weight",
"quantity": "5 g",
"unit": "g"
},
{
"measured_entity": "dried sediment",
"measured_property": null,
"quantity": "0.05 g",
"unit": "g"
},
{
"measured_entity": null,
"measured_property": "fractions",
"quantity": ">250 μm",
"unit": "μm"
},
{
"measured_entity": ">250 μm fractions",
"measured_property": "amorphous organic matter (AOM)",
"quantity": ">90%",
"unit": "%"
},
{
"measured_entity": "dried sediment",
"measured_property": null,
"quantity": "between 20 and 60 g",
"unit": "g"
},
{
"measured_entity": "sieve",
"measured_property": null,
"quantity": "63 μm",
"unit": "μm"
}
] |
measeval | All analyses were carried out at the NERC Isotope Geosciences Laboratory. C and N analyses (from which we present weight % C/N) were performed on 225 samples by combustion in a Costech ECS4010 Elemental Analyser (EA) calibrated against an acetanilide standard (Table S3). C/N atomic ratios were calculated by multiplying by 1.167. Replicate analysis of well-mixed samples indicated a precision of ±<0.1. Carbon isotope analysis was carried out on 289 bulk rock samples (Table S4) after removing migrated hydrocarbons (Stephenson et al., 2005). The hydrocarbons were removed by crushing the rock fragments using a ball mill, and the soluble organic matter from all rock samples was extracted using a Soxhlet extractor. The samples were refluxed for 24 h in an azeotropic mixture of dichloromethane and methanol (93:7, v/v). All materials (cellulose Soxhlet thimbles, silica wool, vials) were cleaned with analytical grade organic solvents prior to use. Any remaining solvent was then removed by evaporation and the dried sediments were transferred to vials. Any calcites (shelly fragments) were removed by placing the samples in 5% HCl overnight before rinsing and drying down. Carbon isotope analysis was also carried out on palynology residues of the >250 μm size fractions dominated by AOM. 13C/12C analyses were performed on 35 samples by combustion in a Costech Elemental Analyser (EA) online to a VG TripleTrap and Optima dual-inlet mass spectrometer, with δ13C values calculated to the VPDB scale using a within-run laboratory standards calibrated against NBS-18, NBS-19, and NBS-22. Replicate 13C/12C analyses were carried out on the section, and the mean standard deviation on the replicate analyses is 0.4‰. | train | S0012821X12004384-1284 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "samples",
"measured_property": null,
"quantity": "225",
"unit": null
},
{
"measured_entity": null,
"measured_property": null,
"quantity": "1.167",
"unit": null
},
{
"measured_entity": "Replicate analysis",
"measured_property": "precision",
"quantity": "±<0.1",
"unit": null
},
{
"measured_entity": "bulk rock samples",
"measured_property": null,
"quantity": "289",
"unit": null
},
{
"measured_entity": "samples",
"measured_property": "were refluxed for",
"quantity": "24 h",
"unit": "h"
},
{
"measured_entity": "azeotropic mixture of dichloromethane and methanol",
"measured_property": null,
"quantity": "93:7, v/v",
"unit": "v/v"
},
{
"measured_entity": "HCl",
"measured_property": null,
"quantity": "5%",
"unit": "%"
},
{
"measured_entity": null,
"measured_property": "size fractions",
"quantity": ">250 μm",
"unit": "μm"
},
{
"measured_entity": "samples",
"measured_property": null,
"quantity": "35",
"unit": null
},
{
"measured_entity": "replicate analyses",
"measured_property": "mean standard deviation",
"quantity": "0.4‰",
"unit": "‰"
}
] |
measeval | A negative carbon isotope excursion of 5‰ has been identified from δ13CTOC and δ13CAOM in an expanded Paleocene–Eocene boundary section from the central North Sea Basin. Palynological (dinoflagellate cyst, pollen, and spore assemblages) and sedimentologic (C/N ratios and kaolinite distribution) evidence indicate major changes occurred to marine and terrestrial environments in NW Europe both preceding and over the CIE. Enhanced halocline stratification and terrigenous input from 4 m before the CIE may indicate tectonic uplift and oceanic restriction of the North Sea, supporting hypotheses for NAIP volcanism as a trigger for the CIE (Svensen et al., 2004), and/or increased terrigenous runoff and regional precipitation, supporting hypotheses of an enhanced hydrologic cycle triggering carbon release (Bice and Marotzke, 2002). A peak in Apectodinium before the CIE is interpreted as an ephemeral increase in terrestrial runoff causing local eutrophication. Further enhanced halocline stratification and terrigenous input at and immediately after the CIE onset, coupled with evidence for sea level rise in coastal areas, indicate possible increased regional precipitation over NW Europe. At this location (paleolatitude 54 oN) increased precipitation would support the hypothesis that a poleward migration of storm tracks from an enhanced hydrologic cycle resulted from global warming during the PETM (Pagani et al., 2006b). | train | S0012821X12004384-1640 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "Paleocene–Eocene boundary section",
"measured_property": "negative carbon isotope excursion",
"quantity": "5‰",
"unit": "‰"
},
{
"measured_entity": "Enhanced halocline stratification and terrigenous input",
"measured_property": null,
"quantity": "from 4 m",
"unit": "m"
},
{
"measured_entity": "location",
"measured_property": "paleolatitude",
"quantity": "54 oN",
"unit": "oN"
}
] |
measeval | Carbon isotopic results of total organic matter (δ13CTOC) and amorphous organic matter (δ13CAOM) against core 22/10a-4 lithology and Apectodinium spp. (%). Blue=bulk rock δ13CTOC; black=δ13CAOM; solid red symbols=bulk rock δ13CTOC from samples with <30% wood/plant tissue (determined from palynological residue of the sample); open red symbols=bulk rock δ13CTOC from samples with >30% wood/plant tissue. The first appearance of Apectodinium augustum identifies the PETM in the North Sea (Bujak and Brinkhuis, 1998), and the first negative shift in δ13C identifies the approximate position of the CIE onset and the Paleocene–Eocene boundary. Values shaded at 2614.7 and 2619.6 m are considered possible outliers based on statistical analysis of the palynological residues (see Section 4.1). Lithologic column shows position of sand intervals (yellow), claystone intervals (brown; predominantly laminated claystone, dark brown), and ash layers (pink). (For interpretation of the references to color in this figure caption, the reader is referred to the web version of this article.) | train | S0012821X12004384-952 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "samples",
"measured_property": "wood/plant tissue",
"quantity": "<30%",
"unit": "%"
},
{
"measured_entity": "samples",
"measured_property": "wood/plant tissue",
"quantity": ">30%",
"unit": "%"
},
{
"measured_entity": "Values shaded",
"measured_property": null,
"quantity": "2614.7 and 2619.6 m",
"unit": "m"
}
] |
measeval | The abrupt onset of Antarctic glaciation during the Eocene–Oligocene Transition (∼33.7 Ma, Oi1) is linked to declining atmospheric pCO2 levels, yet the mechanisms that forced pCO2 decline remain elusive. Biogenic silicon cycling is inextricably linked to both long and short term carbon cycling through the diatoms, siliceous walled autotrophs which today account for up to 40% of primary production. It is hypothesised that during the Late Eocene a sharp rise in diatom abundance could have contributed to pCO2 drawdown and global cooling by increasing the proportion of organic carbon buried in marine sediment. Diatom and sponge silicon isotope ratios (δ30Si) are here combined for the first time to reconstruct the late Eocene–early Oligocene ocean silicon cycle and provide new insight into the role of diatom productivity in Antarctic glaciation. At ODP site 1090 in the Southern Ocean, a 0.6‰ rise in diatom δ30Si through the late Eocene documents increasing diatom silicic acid utilisation with high, near modern values attained by the earliest Oligocene. A concomitant 1.5‰ decline in sponge δ30Si at ODP site 689 on the Maud Rise tracks an approximate doubling of intermediate depth silicic acid concentration in the high southern latitudes. Intermediate depth silicic acid concentration peaked at ∼31.5 Ma, coincident with the final establishment of a deepwater pathway through the Tasman Gateway and Drake Passage. These results suggest that upwelling intensification related to the spin-up of a circum-Antarctic current may have driven late Eocene diatom proliferation. Organic carbon burial associated with higher diatom abundance and export provides a mechanism that can account for pCO2 drawdown not only at, but also prior to, Antarctic glaciation as required by a pCO2 ‘threshold’ mechanism for ice sheet growth. | train | S0012821X13002185-994 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "Oi1",
"measured_property": null,
"quantity": "∼33.7 Ma",
"unit": "Ma"
},
{
"measured_entity": "primary production",
"measured_property": "siliceous walled autotrophs",
"quantity": "up to 40%",
"unit": "%"
},
{
"measured_entity": "diatom δ30Si",
"measured_property": "rise",
"quantity": "0.6‰",
"unit": "‰"
},
{
"measured_entity": "sponge δ30Si",
"measured_property": "decline",
"quantity": "1.5‰",
"unit": "‰"
},
{
"measured_entity": "Intermediate depth silicic acid concentration",
"measured_property": "peaked",
"quantity": "∼31.5 Ma",
"unit": "Ma"
}
] |
measeval | In the Furlo section the CTBI lies within the Scaglia Bianca Formation, which includes abundant biosiliceous limestone. The Livello Bonarelli is a 1 m thick condensed interval of millimetre-laminated black shale and brown radiolarian sand that represents the sedimentary expression of part of OAE 2 (Arthur and Premoli Silva, 1982). Up to 20 m beneath the Bonarelli level there are numerous centimetre scale organic-rich shale layers (Jenkyns et al., 2007). The δ13Corg record has a narrow variation in background values prior to OAE 2, ∼−25.9 to −26.5‰. The characteristic positive excursion in δ13Corg is a 4‰ shift, −27.2 to −23.1‰, occurring within <0.5 m (Fig. 2; Supplementary Material, Table 1e). | train | S0012821X13007309-1691 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "Livello Bonarelli",
"measured_property": "thick",
"quantity": "1 m",
"unit": "m"
},
{
"measured_entity": "numerous centimetre scale organic-rich shale layers",
"measured_property": "beneath the Bonarelli",
"quantity": "Up to 20 m",
"unit": "m"
},
{
"measured_entity": "δ13Corg record",
"measured_property": "narrow variation in background values prior to OAE 2",
"quantity": "∼−25.9 to −26.5‰.",
"unit": "‰"
},
{
"measured_entity": "characteristic positive excursion in δ13Corg",
"measured_property": "shift",
"quantity": "4‰",
"unit": "‰"
},
{
"measured_entity": "characteristic positive excursion in δ13Corg",
"measured_property": "shift",
"quantity": "−27.2 to −23.1‰",
"unit": "‰"
},
{
"measured_entity": "characteristic positive excursion in δ13Corg",
"measured_property": "shift",
"quantity": "within <0.5 m",
"unit": "m"
}
] |
measeval | Sensitivity analysis carried out on MTDATA software, for 9 and 21 trace elements. | train | S0016236113008041-2924 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "trace elements",
"measured_property": null,
"quantity": "9 and 21",
"unit": null
}
] |
measeval | The fate of trace elements was investigated in a 90 kW oxy-combustion pilot plant fed with coal and limestone [7]. It was shown that 82% of elemental Hg was emitted in the exhaust gas, as was 81% of Cl. It was further suggested that the relatively low temperatures, and high Ca content in the system from limestone use promoted condensation and sorption of sulphate, fluoride and chloride species. | train | S0016236113008041-3012 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "oxy-combustion pilot plant",
"measured_property": null,
"quantity": "90 kW",
"unit": "kW"
},
{
"measured_entity": "elemental Hg",
"measured_property": "elemental Hg was emitted in the exhaust gas",
"quantity": "82%",
"unit": "%"
},
{
"measured_entity": "Cl",
"measured_property": null,
"quantity": "81%",
"unit": "%"
}
] |
measeval | Those elements at the higher concentrations in the flue gas include Na, Si, K, Zn, and Br. For Na in particular which was present in reduced amounts in the solid, this suggests that some of it may be partitioning to the flue gas for bed inventories 6 and 13 kg. | train | S0016236113008041-3159 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "bed inventories",
"measured_property": null,
"quantity": "6 and 13 kg",
"unit": "kg"
}
] |
measeval | For Na, Al, K and Fe, the lowest bed inventory of 4.5 kg resulted in a concentration in the flue gas which was less than that of the blank sample, values which then increased for the higher bed inventories. This suggests that a certain amount of these elements is being absorbed, perhaps by the sorbent in the case of Fe, or by the reactor itself. In the case of P, decreased values compared to the blank were found for bed inventories of 4.5 kg and 13 kg, but an increased value for 6 kg inventory. However, the values are very low at <0.01 ppm and this anomalous result may be due to analytical errors. Overall, although concentrations in the flue gas are small at <2 ppm, increasing bed inventory does appear to increase the concentration of the majority of major elements present in the flue gas. | train | S0016236113008041-3161 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "lowest bed inventory",
"measured_property": null,
"quantity": "4.5 kg",
"unit": "kg"
},
{
"measured_entity": "bed inventories",
"measured_property": null,
"quantity": "4.5 kg and 13 kg",
"unit": "kg"
},
{
"measured_entity": "inventory",
"measured_property": null,
"quantity": "6 kg",
"unit": "kg"
},
{
"measured_entity": "P",
"measured_property": "values",
"quantity": "<0.01 ppm",
"unit": "ppm"
},
{
"measured_entity": "flue gas",
"measured_property": "concentrations",
"quantity": "<2 ppm",
"unit": "ppm"
}
] |
measeval | Effect of bed inventory on increase of solid major elemental concentrations for bed inventories of 4.5 kg, 6 kg and 13 kg CaCO3. | train | S0016236113008041-872 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "bed inventories",
"measured_property": null,
"quantity": "4.5 kg, 6 kg and 13 kg",
"unit": "kg"
}
] |
measeval | SEM–EDS analysis of sorbent for reactions undertaken with different SO2 concentrations, showing error bars of +1%. | train | S0016236113008041-961 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "error bars",
"measured_property": null,
"quantity": "+1%",
"unit": "%"
}
] |
measeval | Enceladus, one out of currently 62 satellites of Saturn, orbits the planet at a distance of 3.95 Saturn radii RS (1RS = 60,268 km) and is embedded in the radiation belts of Saturn’s inner magnetosphere. Even with a small radius REnc of only 252 km and an apparent similarity to other icy moons, this moon is by far the most important internal source of dust, neutral gas and plasma in the saturnian system and especially in the magnetosphere. As already inferred from data of the first flybys of the Pioneer 11 (1979), Voyager 1 (1980) and Voyager 2 (1981) spacecraft, respectively (Smith et al., 1981; Krimigis et al., 1982), this moon plays the same role Io does for the jovian system. The Cassini spacecraft, in orbit around Saturn since July 2004, has flown by Enceladus 14 times between 2005 and 2010. Data from Cassini instrument teams identified a plume of water geysers above the so called “tigerstripes” on the south pole of the moon (Dougherty et al., 2006; Porco et al., 2006). This discovery is one of the most important findings to better understand the saturnian magnetosphere. The material released in the form of water gas, ice molecules and dust (Waite et al., 2006; Spahn et al., 2006) from those geysers is the main source of the E-ring and the neutral torus and is the major plasma source of water group ions in the magnetosphere. A summary of Enceladus findings from Cassini measurements is very well presented by Spencer et al. (2009). | train | S0019103511004994-1399 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "Saturn",
"measured_property": null,
"quantity": "62 satellites",
"unit": null
},
{
"measured_entity": "Enceladus",
"measured_property": "distance",
"quantity": "3.95 Saturn radii RS",
"unit": "Saturn radii RS"
},
{
"measured_entity": "1RS",
"measured_property": null,
"quantity": "60,268 km",
"unit": "km"
},
{
"measured_entity": "Enceladus",
"measured_property": "radius REnc",
"quantity": "252 km",
"unit": "km"
},
{
"measured_entity": "Pioneer 11",
"measured_property": "first flybys",
"quantity": "1979",
"unit": null
},
{
"measured_entity": "Voyager 1",
"measured_property": "first flybys",
"quantity": "1980",
"unit": null
},
{
"measured_entity": "Voyager 2",
"measured_property": "first flybys",
"quantity": "1981",
"unit": null
},
{
"measured_entity": "Cassini spacecraft",
"measured_property": "orbit around Saturn",
"quantity": "since July 2004",
"unit": null
},
{
"measured_entity": "Cassini spacecraft",
"measured_property": "flown by Enceladus",
"quantity": "14 times",
"unit": null
},
{
"measured_entity": "Cassini spacecraft",
"measured_property": "flown by Enceladus 14 times",
"quantity": "between 2005 and 2010",
"unit": null
}
] |
measeval | Illustrated electron drift paths for different energies near Enceladus in a perturbed electric field configuration. Only the equatorial plane is shown where x points in the flow direction and y points toward the planet. The energy is mentioned on the top of each figure, The parameter a = 0.1 is the assumed surface velocity of 10% relative to the upstream velocity. | train | S0019103511004994-996 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "parameter a",
"measured_property": null,
"quantity": "0.1",
"unit": null
},
{
"measured_entity": "upstream velocity",
"measured_property": "assumed surface velocity",
"quantity": "10%",
"unit": "%"
}
] |
measeval | H-band slit-viewing VLT images taken on 6 November 2011 UT, just two days before the close approach of BS1 and BS2. (The relatively poor quality of the images arises from optimization of image quality for the science detector, which records spectra, instead of the slit viewing detector. When these images were taken, both detectors could not be in good focus at the same time.) It appears that only BS1 was recorded in this image sequence, perhaps because the filter penetration depth is too shallow to see the apparently deeper BS2 feature, which should have crossed the central meridian about 30 min after BS1. It may also be that BS2 was of lower reflectivity before passing BS1 than it was afterwards (it is quite obvious in the K′ image of Fig. 13). | train | S0019103512001388-1070 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "H-band slit-viewing VLT images",
"measured_property": "taken",
"quantity": "6 November 2011 UT",
"unit": "UT"
},
{
"measured_entity": "H-band slit-viewing VLT images",
"measured_property": "taken",
"quantity": "two days",
"unit": "days"
},
{
"measured_entity": "deeper BS2 feature",
"measured_property": "should have crossed the central meridian",
"quantity": "30 min",
"unit": "min"
}
] |
measeval | The observed peak in the fractional integrated differential brightness of BS1 in the H filter was observed to be 0.64% in the discovery image on 26 October 2011, and declined to 0.02% by December 16. | train | S0019103512001388-3081 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "H filter",
"measured_property": "fractional integrated differential brightness of BS1",
"quantity": "0.64%",
"unit": "%"
},
{
"measured_entity": "discovery image",
"measured_property": null,
"quantity": "26 October 2011",
"unit": null
},
{
"measured_entity": "H filter",
"measured_property": "fractional integrated differential brightness of BS1",
"quantity": "0.02%",
"unit": "%"
},
{
"measured_entity": "differential brightness of BS1 in the H filter",
"measured_property": "declined",
"quantity": "December 16",
"unit": null
}
] |
measeval | Notice also that background ∣B∣ is similar to what is measured at all flybys except R1. During the latter, ∣B∣ was on average 3–4 nT stronger compared to all other flybys. | train | S0019103512002801-1716 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "∣B∣",
"measured_property": "stronger",
"quantity": "average 3–4 nT",
"unit": "nT"
}
] |
measeval | Observed deviations from the typical, plasma-absorbing interaction region profile may explain some differences. These deviations do not necessarily mean the main interaction mode at Rhea is not plasma absorption. For instance, Simon et al. (2012) demonstrates that the combination of low magnetosonic Mach number and the high plasma beta values of Rhea’s space environment amplifies interaction features which for other plasma absorbers (e.g. Tethys, Dione, Earth’s Moon) are barely detectable. One of these features is a stronger, flow-aligned magnetic field component perturbation, which gives rise to mass-loading-like interaction signatures. Such structures may also complicate electron drifts, but to what extent it is uncertain. Similarly, the high plasma beta makes surface charging more important for Rhea compared to the other Saturnian moons (Roussos et al., 2010). Furthermore, recent developments in the study of Earth’s Moon interaction with the solar wind indicate that the standard picture for a lunar-type interaction may be too simplified: processes, such as the entry of exospheric pick-up ions in the center of the wake, backscattering on the surface and self-pick up of ambient plasma ions, appear to also have an impact on the wake dynamics (Halekas et al., 2011). For instance, the self-pick up process has been shown to lead to enhanced ULF wave activity in the lunar, at least 10% of the time (Nakagawa et al., 2012). Whether a similar process is important the inferred instability at Rhea is questionable, as the latter appears to operate continuously. | train | S0019103512002801-2018 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "features",
"measured_property": null,
"quantity": "One",
"unit": null
},
{
"measured_entity": "enhanced ULF wave activity in the lunar",
"measured_property": "self-pick up process has been shown to lead to",
"quantity": "at least 10%",
"unit": "%"
}
] |
measeval | A new addition to the thermospheric energy equation is the inclusion of H3+ cooling, a process known to be important on Jupiter (Miller et al., 2006, 2010). At thermospheric temperatures typically found on Saturn (320–500 K, Nagy et al., 2009), we do not expect H3+ cooling to play an important role, but we included the process to be able to assess its importance for cases where polar magnetospheric heating raises temperatures above ∼500 K. We implemented globally the H3+ cooling rates of Miller et al. (2010) in the form of a parameterisation as a function of local thermospheric temperature and H3+ density. | train | S0019103512003533-3306 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "Saturn",
"measured_property": "thermospheric temperatures",
"quantity": "320–500 K",
"unit": "K"
},
{
"measured_entity": "Saturn",
"measured_property": "temperatures",
"quantity": "above ∼500 K",
"unit": "K"
}
] |
measeval | Density profiles O and H based on the C2 model (Paper I) and the density profile of H based on the empirical model of K10 with a mean temperature of 7200 K. | train | S0019103512003995-1283 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "empirical model of K10",
"measured_property": "mean temperature",
"quantity": "7200 K",
"unit": "K"
}
] |
measeval | Moutou et al. (2001) looked for absorption by species such as Na,H,He,CH+,CO+,N2+, and H2O+ in the upper atmosphere of HD209458b. These observations were followed by Moutou et al. (2003) who attempted to measure the transit depth in the He 1083 nm line that was predicted to be significant by Seager and Sasselov (2000). The most recent searches were reported by Winn et al. (2004) and Narita et al. (2005) who looked for transits in the Na D, Li, Hα, Hβ, Hγ, Fe, and Ca absorption lines. So far none of the ground-based searches have led to a detection of the upper atmosphere. However, the non-detection is based on only a few observations that have proven difficult to analyze, and the search should continue. | train | S0019103512003995-1767 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "He",
"measured_property": "line",
"quantity": "1083 nm",
"unit": "nm"
}
] |
measeval | The cutoff level of the empirical model is somewhat arbitrary. For neutral species it is partly based on ionization (K10), but this criterion obviously does not apply to ions. With a cutoff level at 5Rp for the ions only, the M7 model yields line-integrated transit depths of 3.9%, 8%, and 5.8% in the C II 1334.5 Å, C II 1335.7 Å, and Si III lines, respectively, if 40% of silicon is Si2+. These values agree with the observed values to better than 2σ. Similarly, by extending the cutoff level of the C2 model to 5Rp, we obtained transit depths of 3.2%, 6.7%, and 4.6% in the C II 1334.5 Å, C II 1335.7 Å, and Si III lines, respectively (see Table 2). These values deviate from the observed values by 2σ, 0.9σ, and 2.6σ, respectively. The transit depths predicted by the M7 model are higher partly because the mean temperature of 8250 K is higher than the corresponding temperature in the C2 model (Paper I). This also leads to the higher Si2+/Si+ ratio that we used here. | train | S0019103512003995-2681 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "M7 model",
"measured_property": "cutoff level",
"quantity": "5Rp",
"unit": "Rp"
},
{
"measured_entity": "M7 model",
"measured_property": "line-integrated transit depths",
"quantity": "3.9%",
"unit": "%"
},
{
"measured_entity": "M7 model",
"measured_property": "line-integrated transit depths",
"quantity": "8%",
"unit": "%"
},
{
"measured_entity": "M7 model",
"measured_property": "line-integrated transit depths",
"quantity": "5.8%",
"unit": "%"
},
{
"measured_entity": "silicon",
"measured_property": "Si2+",
"quantity": "40%",
"unit": "%"
},
{
"measured_entity": "C2 model",
"measured_property": "cutoff level",
"quantity": "5Rp",
"unit": "Rp"
},
{
"measured_entity": "C2 model",
"measured_property": "transit depths",
"quantity": "3.2%",
"unit": "%"
},
{
"measured_entity": "C2 model",
"measured_property": "transit depths",
"quantity": "6.7%",
"unit": "%"
},
{
"measured_entity": "C2 model",
"measured_property": "transit depths",
"quantity": "4.6%",
"unit": "%"
},
{
"measured_entity": "M7 model",
"measured_property": "mean temperature",
"quantity": "8250 K",
"unit": "K"
}
] |
measeval | In Paper I we noted that the stellar XUV flux, or the corresponding alternative heat source, would have to be 5–10 stronger than the average solar flux to produce a mean temperature between 8000 and 9000 K. Under such circumstances, the predicted transit depths in the C II and Si III lines would obviously be even higher than the values predicted by the SOL2 model. Indeed, higher temperatures broaden absorption in the wings of the line profiles and may help to explain the in-transit flux differences better (see Fig. 7). However, the energy input and temperature in the model cannot be increased without bound. Higher temperatures and flux lead to more efficient ionization of the neutral species, and as a result the transit depths in the H Lyman α and O I lines begin to decrease. Also, mass loss rates of 109–1010 kg s−1 lead to the loss of 10–100% of the planet’s mass over the estimated lifetime of the system, and this probably limits reasonable energy inputs to less than ∼10 times the solar average on HD209458b. | train | S0019103512003995-2737 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "stellar XUV flux, or the corresponding alternative heat source",
"measured_property": "stronger",
"quantity": "5–10",
"unit": null
},
{
"measured_entity": "mean temperature",
"measured_property": null,
"quantity": "between 8000 and 9000 K",
"unit": "K"
},
{
"measured_entity": "mass",
"measured_property": "loss rates",
"quantity": "109–1010 kg s−1",
"unit": "kg s−1"
},
{
"measured_entity": "planet’s mass",
"measured_property": "loss",
"quantity": "10–100%",
"unit": "%"
},
{
"measured_entity": "solar average on HD209458b",
"measured_property": "energy inputs",
"quantity": "less than ∼10 times",
"unit": "times"
}
] |
measeval | The detections of atomic hydrogen, heavy atoms and ions surrounding the extrasolar giant planet (EGP) HD209458b constrain the composition, temperature and density profiles in its upper atmosphere. Thus the observations provide guidance for models that have so far predicted a range of possible conditions. We present the first hydrodynamic escape model for the upper atmosphere that includes all of the detected species in order to explain their presence at high altitudes, and to further constrain the temperature and velocity profiles. This model calculates the stellar heating rates based on recent estimates of photoelectron heating efficiencies, and includes the photochemistry of heavy atoms and ions in addition to hydrogen and helium. The composition at the lower boundary of the escape model is constrained by a full photochemical model of the lower atmosphere. We confirm that molecules dissociate near the 1 μbar level, and find that complex molecular chemistry does not need to be included above this level. We also confirm that diffusive separation of the detected species does not occur because the heavy atoms and ions collide frequently with the rapidly escaping H and H+. This means that the abundance of the heavy atoms and ions in the thermosphere simply depends on the elemental abundances and ionization rates. We show that, as expected, H and O remain mostly neutral up to at least 3Rp, whereas both C and Si are mostly ionized at significantly lower altitudes. We also explore the temperature and velocity profiles, and find that the outflow speed and the temperature gradients depend strongly on the assumed heating efficiencies. Our models predict an upper limit of 8000 K for the mean (pressure averaged) temperature below 3Rp, with a typical value of 7000 K based on the average solar XUV flux at 0.047 AU. We use these temperature limits and the observations to evaluate the role of stellar energy in heating the upper atmosphere. | train | S0019103512004009-2821 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "molecules",
"measured_property": "dissociate",
"quantity": "1 μbar",
"unit": "μbar"
},
{
"measured_entity": "H and O",
"measured_property": "emain mostly neutral",
"quantity": "up to at least 3Rp",
"unit": "Rp"
},
{
"measured_entity": "models predict",
"measured_property": "mean (pressure averaged) temperature",
"quantity": "8000 K",
"unit": "K"
},
{
"measured_entity": "models predict",
"measured_property": null,
"quantity": "3Rp",
"unit": "Rp"
},
{
"measured_entity": "models predict",
"measured_property": "mean (pressure averaged) temperature",
"quantity": "7000 K",
"unit": "K"
},
{
"measured_entity": "models predict",
"measured_property": "average solar XUV flux",
"quantity": "0.047 AU",
"unit": "AU"
}
] |
measeval | The effect of changing the heating efficiency on the velocity profile is quite dramatic. As ηnet ranges from 0.1 to 1 (with the average solar flux), the velocity at the upper boundary increases from 2.6 km s−1 to 25 km s−1. However, the velocity does not increase linearly with stellar flux or without a bound – in the 100× case the velocity at the upper boundary is only 30 km s−1. An interesting qualitative feature of the solutions is that the sonic point moves to a lower altitude with increasing heating efficiency or stellar flux. With ηnet = 0.1 the isothermal sonic point is located above the upper boundary whereas with ηnet = 1 it is located at 4Rp. This behavior is related to the temperature gradient and it is discussed further in Section 3.1.3. Basically the sonic point, when it exists, moves further from the planet as the high altitude heating rate decreases. | train | S0019103512004009-3976 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "heating efficiency",
"measured_property": "ηnet",
"quantity": "from 0.1 to 1",
"unit": null
},
{
"measured_entity": "upper boundary",
"measured_property": "velocity",
"quantity": "increases from 2.6 km s−1 to 25 km s−1",
"unit": "km s−1"
},
{
"measured_entity": "case",
"measured_property": null,
"quantity": "100×",
"unit": "×"
},
{
"measured_entity": "upper boundary",
"measured_property": "velocity",
"quantity": "30 km s−1",
"unit": "km s−1"
},
{
"measured_entity": "heating efficiency",
"measured_property": "ηnet",
"quantity": "0.1",
"unit": null
},
{
"measured_entity": "ηnet",
"measured_property": null,
"quantity": "1",
"unit": null
},
{
"measured_entity": "isothermal sonic point",
"measured_property": "located",
"quantity": "4Rp",
"unit": "Rp"
}
] |
measeval | The substellar tide is included in the C3 model. We included it mainly to compare our results with previous models (Garcia Munoz, 2007; Penz et al., 2008; Murray-Clay et al., 2009). The substellar tide is not a particularly good representation of the stellar tide in a globally averaged sense. In reality, including tides in the models is much more complicated than simply considering the substellar tide (e.g., Trammell et al., 2011). Compared to the C1 model, the maximum temperature in the C3 model is cooler by ∼1000 K and at high altitudes the C3 model is cooler by 1000–2000 K. The velocity is faster and hence adiabatic cooling is also more efficient. The substellar tide drives supersonic escape (see also, Penz et al., 2008) and the sonic point in the C3 model is at a much lower altitude than in the C1 model (see Section 3.1.3). However, it is not clear how the sonic point behaves as a function of latitude and longitude. Given that the tide is also likely to induce horizontal flows, it cannot be included accurately in 1D models. | train | S0019103512004009-4350 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "C3 model",
"measured_property": "maximum temperature",
"quantity": "∼1000 K",
"unit": "K"
},
{
"measured_entity": "C3 model",
"measured_property": "maximum temperature",
"quantity": "1000–2000 K",
"unit": "K"
}
] |
measeval | The H/H+ transition in the MC09 model occurs near 1.4Rp. If we replace the gray approximation with the full solar spectrum in this model, the H/H+ transition moves higher to 2–3Rp. This is because photons with different energies penetrate to different depths in the atmosphere, extending the heating profile in altitude around the heating peak. This is why the temperature at the 30 nbar level in the C2 model is 3800 K and not 1000 K. In order to test the effect of higher temperatures in the lower thermosphere, we extended the MC09 model to p0 = 1 μbar (with T0 = 1300 K) and again used the full solar spectrum for heating and ionization. With these conditions, the H/H+ transition moves up to 3.4Rp, in agreement with the C2 model. We conclude that the unrealistic boundary conditions and the gray approximation adopted by Murray-Clay et al. (2009) and Guo (2011) lead to an underestimated overall density of H and an overestimated ion fraction. Thus their density profiles yield a H Lyman α transit depth of the order of 2–3% i.e., not significantly higher than the visible transit depth. | train | S0019103512004009-5033 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "H/H+",
"measured_property": "transition",
"quantity": "near 1.4Rp",
"unit": "Rp"
},
{
"measured_entity": "H/H+",
"measured_property": "transition",
"quantity": "2–3Rp",
"unit": "Rp"
},
{
"measured_entity": "C2 model",
"measured_property": "level",
"quantity": "30 nbar",
"unit": "nbar"
},
{
"measured_entity": "C2 model",
"measured_property": "temperature",
"quantity": "3800 K",
"unit": "K"
},
{
"measured_entity": "C2 model",
"measured_property": "temperature",
"quantity": "1000 K.",
"unit": "K"
},
{
"measured_entity": "MC09 model",
"measured_property": "p0",
"quantity": "1 μbar",
"unit": "μbar"
},
{
"measured_entity": "MC09 model",
"measured_property": "T0",
"quantity": "1300 K",
"unit": "K"
},
{
"measured_entity": "H/H+",
"measured_property": "transition",
"quantity": "3.4Rp",
"unit": "Rp"
},
{
"measured_entity": "H Lyman α transit depth",
"measured_property": null,
"quantity": "2–3%",
"unit": "%"
}
] |
measeval | We calculated the collision frequencies based on the C2 model, and found that collisions with neutral H dominate the transport of heavy neutral atoms such as O below 3.5Rp. At altitudes higher than this, collisions with H+ are more frequent. In Paper II we demonstrate that a mass loss rate of 6 × 106 kg s−1 is required to prevent diffusive separation of O (the heaviest neutral species detected so far) in the thermosphere. The mass loss rate in our models is Ṁ>107kgs-1 and thus O is dragged along to high altitudes by H. On the other hand, collisions with H+ dominate the transport of heavy ions such as Si+ as long as the ratio [H+]/[H] ≳ 10−4 (Paper II). This explains why Coulomb collisions in our models are more frequent than heavy ion–H collisions at almost all altitudes apart from the immediate vicinity of the lower boundary. These collisions are much more efficient in preventing diffusive separation than collisions with neutral H. | train | S0019103512004009-5271 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "collisions with neutral H dominate the transport of heavy neutral atoms",
"measured_property": null,
"quantity": "below 3.5Rp",
"unit": "Rp"
},
{
"measured_entity": "mass",
"measured_property": "oss rate",
"quantity": "6 × 106 kg s−1",
"unit": "kg s−1"
},
{
"measured_entity": "models",
"measured_property": "Ṁ",
"quantity": ">107kgs-1",
"unit": "kgs-1"
},
{
"measured_entity": "[H+]/[H]",
"measured_property": "ratio",
"quantity": "≳ 10−4",
"unit": null
}
] |
measeval | The Wet Chemistry Laboratory (WCL) on the Phoenix Lander provided in situ measurements of the composition of soluble salts in the martian soil. Soluble sulfate was present at 1.3 ± 0.5 wt.% (Kounaves et al., 2010b), along with cations of sodium, potassium, calcium and magnesium. The most surprising result was the presence of perchlorate (ClO4-) at an inferred concentration in the soil of ∼0.5 wt.% (Hecht et al., 2009; Kounaves et al., 2010a). The dominance of Mg(ClO4)2 is consistent with simulations of evaporation and freezing at the Phoenix landing site (Marion et al., 2010); however, further analysis of data from the WCL suggests that Ca(ClO4)2 may be the dominant form of perchlorate (Kounaves et al., 2012). The perchlorate-sensitive electrode in the WCL experiment was also sensitive to nitrate, but it was 1000 times more sensitive to perchlorate. Thus, the methodology precluded the detection of nitrate because the signal would have required a mass of nitrate that exceeded the mass of the sample (Hecht et al., 2009). Recently, the MSL mission has also confirmed the presence of perchlorate using pyrolysis as part of the SAM experiment (Steininger et al., 2013). Specifically, pyrolysis showed release of chloromethane and O2 from heated soil samples at the Rocknest location, which is consistent with the decomposition of perchlorate (Sutter et al., 2013). If all of the evolved O2 was released from perchlorate, then the samples contained a comparable amount of perchlorate to the samples at the Phoenix landing site (Leshin et al., 2013). Furthermore, reanalysis of the Viking thermal volatilization experiments suggest ⩽1.6% perchlorate at both Viking 1 and Viking 2 landing sites (Navarro-Gonzalez et al., 2010); however, this has been subject to some debate (Biemann and Bada, 2011). Native perchlorate has also recently been measured in the martian meteorite EETA79001, albeit at a level <1 ppm by mass (Kounaves et al., 2014). Given the various locations of possible detection, perchlorate appears to be ubiquitous on the martian surface. | train | S0019103513005058-3154 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "Soluble sulfate",
"measured_property": null,
"quantity": "1.3 ± 0.5 wt.%",
"unit": "wt.%"
},
{
"measured_entity": "soil",
"measured_property": "perchlorate (ClO4-)",
"quantity": "∼0.5 wt.%",
"unit": "wt.%"
},
{
"measured_entity": "perchlorate-sensitive electrode",
"measured_property": "sensitive to nitrate",
"quantity": "1000 times",
"unit": "times"
},
{
"measured_entity": "Viking 1 and Viking 2 landing sites",
"measured_property": "perchlorate",
"quantity": "⩽1.6%",
"unit": "%"
},
{
"measured_entity": "martian meteorite EETA79001",
"measured_property": "Native perchlorate",
"quantity": "<1 ppm by mass",
"unit": "ppm by mass"
}
] |
measeval | Most gases are removed from the atmosphere to the surface according to a prescribed deposition velocity. The deposition velocity is a scaling factor that affects the transport of species from the bulk atmosphere to the surface in the absence of rain. The deposition velocity is coupled to the gas concentrations computed by chemical kinetics. Following Zahnle et al. (2008), we apply a deposition velocity of 0.02 cm s−1 to all species, with two exceptions. First, the deposition velocities for O2, H2, and CO are set to zero (following Zahnle et al., 2008). Second, all species with a zero deposition velocity in Catling et al. (2010) are prescribed a zero deposition velocity because we consider them to be nonreactive. These species include NO3, N2O5, N2O, CH3O, CH3ONO, CH3ONO2, CH2ONO2, CH3O2, CH3OH, CH2OOH, Cl2, CH2OH, CH2O2, OClO, ClOO, ClONO, ClNO, ClNO2, CH3OCl, Cl2O2, Cl2O, ClO3, and Cl2O4. The deposition velocity multiplied by the species number density at the lower boundary, in addition to the flux term from eddy diffusion, determines the flux of species to the surface. | train | S0019103513005058-3917 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "all species",
"measured_property": "deposition velocity",
"quantity": "0.02 cm s−1",
"unit": "s−1"
},
{
"measured_entity": "exceptions",
"measured_property": null,
"quantity": "two",
"unit": null
},
{
"measured_entity": "O2, H2, and CO",
"measured_property": "deposition velocities",
"quantity": "zero",
"unit": null
},
{
"measured_entity": "all species",
"measured_property": "deposition velocity",
"quantity": "zero",
"unit": null
},
{
"measured_entity": "all species",
"measured_property": "deposition velocity",
"quantity": "zero",
"unit": null
}
] |
measeval | Atmospheric temperatures affect photochemical rate constants and atmospheric water vapor content. These, in turn, affect the chain of reaction rates that lead to the oxidation of Cl to form HClO4. To pinpoint how atmospheric temperatures alter reaction rates, we shift the nominal Mars temperature profile to higher surface temperatures. The shape of the profile is preserved, but the surface temperature is increased from 211 K to 284 K (a temperature increase of ∼35%), with the latter temperature being more representative of surface temperatures on Earth. | train | S0019103513005058-4098 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "surface temperature",
"measured_property": "increased",
"quantity": "from 211 K to 284 K",
"unit": "K"
},
{
"measured_entity": "temperature",
"measured_property": "increase",
"quantity": "∼35%",
"unit": "%"
}
] |
measeval | We next make several assumptions to calculate the concentration of salts that have accumulated in the soil during the Amazonian eon. We first assume that perchloric acid, sulfate aerosols, nitric acid and pernitric acid have accumulated at a uniform rate. This assumption is valid because the lack of aqueous minerals and very low weathering rates tell us the Amazonian eon on Mars has been characterized by a climate and atmosphere not greatly different from today (Bibring et al., 2006). We next assume a range of soil mixing depths so that salts are distributed throughout the soil column. According to Zent (1998), small post-Noachian impactors have churned the soil on Mars to a 1/e mixing depth of 0.51–0.85 m. Taking three e-folding depths, the range would be 1.5–2.6 m depth, with a mean ∼2 m. The last assumption we make is that the soil density is 1 g cm−3 (Moore and Jakosky, 1989). Using these assumptions, we calculate the concentrations of anions in the soil for the nominal model and report them in Table 4. These anions must be combined into salts. For perchlorate, the salts may be Mg(ClO4)2 or Ca(ClO4)2 as discussed earlier. The dominant nitrogen-bearing salt is unknown. | train | S0019103513005058-4158 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": null,
"measured_property": null,
"quantity": "1/e",
"unit": null
},
{
"measured_entity": "impactors have churned the soil on Mars",
"measured_property": "1/e mixing depth",
"quantity": "0.51–0.85 m.",
"unit": "m"
},
{
"measured_entity": "e-folding depths",
"measured_property": null,
"quantity": "three",
"unit": null
},
{
"measured_entity": "three e-folding depths",
"measured_property": "depth",
"quantity": "1.5–2.6 m",
"unit": "m"
},
{
"measured_entity": "three e-folding depths",
"measured_property": "depth",
"quantity": "∼2 m.",
"unit": "m"
},
{
"measured_entity": "soil",
"measured_property": "density",
"quantity": "1 g cm−3",
"unit": "g cm−3"
}
] |
measeval | The sulfate deposition flux produced in the nominal model is compatible with estimates of the amount of sulfates on Mars. The nominal range (1.0–1.7 wt.% SO4) is consistent with 1.3 wt.% soluble sulfate measured at the Phoenix landing site (Kounaves et al., 2010b). The estimates also compare well with an average ∼6.8 wt.% sulfur as SO3 (2.7 wt.% S) in global soil inferred from elemental abundances measured at various locations on Mars by Spirit, Opportunity, Pathfinder, and Viking Landers. We can also compare the average sulfur content in the top few tens of centimeters of the soil of ∼4.4 wt.% inferred from Gamma Ray Spectrometer measurements (King and McLennan, 2010). The agreement between model results and data suggest that 0.1% of the terrestrial volcanic gas flux is a good estimate for the volcanic emission rate on Mars 1–2 Ga if soil salts derive from volcanic input. | train | S0019103513005058-4175 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "global soil",
"measured_property": "SO4",
"quantity": "1.0–1.7 wt.%",
"unit": "wt.%"
},
{
"measured_entity": "soil",
"measured_property": "soluble sulfate",
"quantity": "1.3 wt.%",
"unit": "wt.%"
},
{
"measured_entity": "global soil",
"measured_property": "SO3",
"quantity": "average ∼6.8 wt.%",
"unit": "wt.%"
},
{
"measured_entity": "global soil",
"measured_property": "S",
"quantity": "2.7 wt.%",
"unit": "wt.%"
},
{
"measured_entity": "soil",
"measured_property": null,
"quantity": "top few tens of centimeters",
"unit": "centimeters"
},
{
"measured_entity": "soil",
"measured_property": "average sulfur content",
"quantity": "∼4.4 wt.%",
"unit": "wt.%"
},
{
"measured_entity": "terrestrial volcanic gas flux",
"measured_property": null,
"quantity": "0.1%",
"unit": "%"
}
] |
measeval | As stated previously, there is considerable uncertainty in the input rate of odd nitrogen (N and NO) species from the martian ionosphere to the neutral atmosphere (Krasnopolsky, 1993). In his own model of the neutral atmosphere, Krasnopolsky considers cases both with and without input of odd nitrogen from the upper atmosphere (Krasnopolsky, 1993). We vary the input of N and NO into the model, using the nominal case as an upper limit. As these values are decreased, the pernitrate deposition flux drops, which is shown in Fig. 5. The lowest input of odd nitrogen corresponds to 3.5–6.1 (×10−4) wt.% N accumulated over 3 byr and mixed into 1.5–2.6 m of soil. | train | S0019103513005058-4302 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "N",
"measured_property": "input of odd nitrogen",
"quantity": "3.5–6.1 (×10−4) wt.%",
"unit": "wt.%"
},
{
"measured_entity": "input of odd nitrogen",
"measured_property": "accumulated",
"quantity": "over 3 byr",
"unit": "byr"
},
{
"measured_entity": "soil",
"measured_property": null,
"quantity": "1.5–2.6 m",
"unit": "m"
}
] |
measeval | To semi-quantitatively assess the sensitivity of the deposition fluxes of salts to temperature, we forced the model temperature profile to higher values by increasing the temperature profile by 35% in 5% increments. Warmer temperatures significantly increase the formation of perchloric acid. Over the range tested, the deposition rate of perchloric acid increases by around six orders of magnitude. | train | S0019103513005058-4349 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "temperature profile",
"measured_property": null,
"quantity": "35%",
"unit": "%"
},
{
"measured_entity": "temperature profile",
"measured_property": "increments",
"quantity": "5%",
"unit": "%"
},
{
"measured_entity": "perchloric acid",
"measured_property": "deposition rate",
"quantity": "around six orders of magnitude",
"unit": "orders of magnitude"
}
] |
measeval | To obtain a relatively stable substrate during the measurements, all iron substrates were pre-conditioned in a 10 wt.% NaOH solution overnight to form a thin oxidized surface layer, followed by rinsing with 99.5% pure ethanol and drying with a gentle stream of nitrogen gas prior to use. The alkaline treatment of the iron surface results in formation of hematite (α-Fe2O3), which will be shown and discussed in relation to Fig. 9. The hematite surface contains different types of surface hydroxyl groups that differ by their coordination to the substrate [26]. The hematite surface is amphoteric due to the possibility of protonization and deprotonization of the surface hydroxyl groups. In neutral solution, the ξ-potential has been found to be slightly positive [27,28]. The water contact angle on our hematite surface is 69 ± 3°, and the open circuit potential relative to Ag/AgCl is −0.72 V, which is similar to that of iron due to the small thickness of the oxide layer. | train | S0021979713004438-1401 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "solution",
"measured_property": "NaOH",
"quantity": "10 wt.%",
"unit": "wt.%"
},
{
"measured_entity": "pure ethanol",
"measured_property": null,
"quantity": "99.5%",
"unit": "%"
},
{
"measured_entity": "water",
"measured_property": "contact angle",
"quantity": "69 ± 3°",
"unit": "°"
},
{
"measured_entity": "hematite surface",
"measured_property": "open circuit potential",
"quantity": "−0.72 V",
"unit": "V"
}
] |
measeval | Tinnitus is the perception of sounds in the head or ears, usually defined as a ringing, buzzing or whistling sound. Tinnitus can be objective or subjective. Objective tinnitus is caused by sounds generated by an internal biological activity. However, subjective tinnitus is much more common and results from abnormal neural activities which are not formed by sounds [1]. Subjective tinnitus is a common and disturbing phenomenon, with a reported prevalence ranging from 7 to 20% [2–5] in the general population, and an estimated 10 year incidence rate in adults aged over 48 years of 13% [6]. Approximately 5% of the population is severely affected by their tinnitus [7], for example experiencing sleep disorders, concentration difficulties, and symptoms of anxiety and depression. | train | S0022399913003358-931 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "general population",
"measured_property": "prevalence",
"quantity": "7 to 20%",
"unit": "%"
},
{
"measured_entity": "ncidence rate",
"measured_property": null,
"quantity": "10 year",
"unit": "year"
},
{
"measured_entity": "adults",
"measured_property": null,
"quantity": "over 48 years",
"unit": "years"
},
{
"measured_entity": "adults aged over 48 years",
"measured_property": "10 year incidence rate",
"quantity": "13%",
"unit": "%"
},
{
"measured_entity": "population",
"measured_property": "severely affected by their tinnitus",
"quantity": "Approximately 5%",
"unit": "%"
}
] |
measeval | Personality characteristics previously reported to be associated with tinnitus include hysteria and hypochondriasis [9,12], introversion [13], withdrawal [9], and emotional isolation [14]. Additionally, particular cognitive strategies, for example, dysfunctional and catastrophic thoughts can increase patients' emotional distress and perceived tinnitus severity, and are thought to be closely related to personality factors [15]. Neuroticism is expressed as “individual differences in the tendency to experience negative, distressing emotions” [16] (p. 301). At one extreme, individuals are characterized by high levels of vulnerability to experience negative emotions, including sadness, fear, anxiety, anger, frustration, and insecurity [17]. At the other end of the spectrum, individuals who score low in neuroticism are more emotionally stable and less reactive to stress. Neuroticism has been associated with adverse outcomes in various health conditions, including increased likelihood of morbidity in those with testicular cancer [18], and an increased likelihood of arthritis, kidney/liver disease, and diabetes in the general population [19]. There is evidence that neurotic traits are stronger in tinnitus patients [20], particularly in those with higher levels of tinnitus annoyance, and recent evidence that neuroticism may predict the development of severe tinnitus in patients already experiencing some tinnitus [21]. In a cross-sectional sample of 530 participants (50% with chronic tinnitus), Bartels and colleagues [22] studied the role of type D personality (the tendency towards negative affectivity and social inhibition) on health-related quality of life and self-reported tinnitus-related distress. Tinnitus patients with type D personality reported greater tinnitus-related distress and poorer health-related quality of life compared to those with other personality types. The authors concluded that some personality characteristics are associated with having tinnitus and are likely to contribute to its perceived severity. | train | S0022399913003358-943 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "cross-sectional sample",
"measured_property": null,
"quantity": "530 participants",
"unit": "participants"
},
{
"measured_entity": "530 participants",
"measured_property": "with chronic tinnitus",
"quantity": "50%",
"unit": "%"
}
] |
measeval | SQUID magnetometry measurements revealed that both samples 1 and 2 exhibit weak temperature independent paramagnetic behaviour (typically χM∼5×10−4 emu mol−1) between 5 and 300 K. This behaviour is commensurate with other alkaline earth nitride halides and suggests either weakly paramagnetic materials or intrinsically diamagnetic materials with very small levels of alkaline earth metal impurities (below the detection limit of PXD and PND) [15,16]. (A plot of χM vs. T for 1 and 2 is available as Supplementary information.) | train | S0022459611006116-1448 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "samples 1 and 2",
"measured_property": "temperature",
"quantity": "between 5 and 300 K",
"unit": "K"
}
] |
measeval | Refined crystallographic parameters for (1) from PXD and PND at 298 K. | train | S0022459611006116-547 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "Refined crystallographic parameters for (1) from PXD and PND",
"measured_property": null,
"quantity": "298 K.",
"unit": "K"
}
] |
measeval | ► We examine a high resolution multi-proxy physical properties from two marine cores. ► Little correlation between physical proxies and climate in early Holocene ► Reworking probable cause of poor correlation in Early Holocene ► Possible anthropogenic influence on sedimentation in the last 200 years | train | S0025322712001600-2230 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "marine cores",
"measured_property": null,
"quantity": "two",
"unit": null
},
{
"measured_entity": "sedimentation",
"measured_property": null,
"quantity": "last 200 years",
"unit": "years"
}
] |
measeval | Analysis of the diffraction data was conducted by measuring peak intensity as peak area using Bruker Diffrac Plus EVA-12.0 software. Estimates of mineral composition were made by a reference intensity ratio method based on factors calculated with the Newmod programme as described in Hillier (2003). Illite crystallinity was measured using the full width at half maximum (FHWM) of the 001 basal illite peak and integral breadth (I Breadth) of the same peak (Kübler and Jaboyedoff, 2000). Both measurements are measured as values of ∆2°θ and show identical trends (Alizai et al., 2012). Because our cores are < 9 m long, post-depositional burial diagenesis should not be a significant factor in clay mineral composition. Where clay mineral values are greater than 10% uncertainty is estimated as better than 5% weight at the 95% confidence level (Hillier, 2003). Clay mineral estimates are shown in Table 1. | train | S0025322712001600-2406 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "cores",
"measured_property": "long",
"quantity": "< 9 m",
"unit": "m"
},
{
"measured_entity": "clay mineral",
"measured_property": "values",
"quantity": "greater than 10%",
"unit": "%"
},
{
"measured_entity": "uncertainty",
"measured_property": "weight",
"quantity": "better than 5%",
"unit": "%"
},
{
"measured_entity": "values",
"measured_property": "confidence level",
"quantity": "95%",
"unit": "%"
}
] |
measeval | The aim of the current investigation was to determine whether fungal and bacterial species richness would affect the development of soil structural properties (e.g. aggregate stability and pore size) over a 7-month period and establish whether changes in genetic composition would be brought about by the presence of roots (either mycorrhizal or non-mycorrhizal). Since the premise of the investigation was to quantify the relationship between biological richness and soil structural changes over time, the soils were not pre-incubated prior to the start of the experiment. Therefore, microbial communities were allowed to develop during the course of the 7 month experiment either in the presence of mycorrhizal or non-mycorrhizal roots, or in unplanted soil, thereby allowing root associated changes in community development to be measured. Others, for example Griffiths et al. (2001) and Wertz et al. (2006), incubated soils for 9 or 4.8 months respectively to allow microbial communities to develop a similar biomass before biodiversity/function relationships were studied. In this investigation, the progression of soil structural development together with microbial compositional changes over time and in tandem with root development was characterised. | train | S0031405612000728-1621 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "current investigation",
"measured_property": "period",
"quantity": "over a 7-month",
"unit": "month"
},
{
"measured_entity": "experiment",
"measured_property": "course",
"quantity": "7 month",
"unit": "month"
},
{
"measured_entity": "soils",
"measured_property": "incubated",
"quantity": "9",
"unit": "months"
},
{
"measured_entity": "soils",
"measured_property": "incubated",
"quantity": "4.8 months",
"unit": "months"
}
] |
measeval | In the present study, both aggregate stability and repellency were reduced in month 7; specifically the degree of reduction in repellency was less in the mycorrhizal soils than in the non-mycorrhizal soils. In the mycorrhizal soils, aggregate water repellency was also negatively correlated with bacterial (and fungal) TRF richness but positively correlated with root size and microbial biomass-C. It is likely that mycorrhizal hyphae contributed to the microbial biomass-C measured here which might explain why microbial biomass-C was not a factor in the model explaining repellency in the NM soils. In the mycorrhizal soils the relationship between microbial biomass-C and aggregate stability was negative, whilst it was positive for repellency. The GLM regressions used data for all 7 months but the system was dynamic across the months. For example, aggregate stability was greater in the mycorrhizal soils in month 3, yet repellency increased in months 5 and 7. The positive relationship observed between per cent root length colonised and microbial biomass-C is likely to be the result of increasing hyphal length in the soil, or possibly an enhancement of other microbial species too, since internal AMF root colonisation may not reflect the extraradical hyphal biomass. Aggregate turnover rates range from 4 to 88 days (De Gryze et al. 2005, 2006); an increase in aggregate stability observed here over a 60 day period (from the first to third month harvest) and an increase in aggregate water repellency over a 120 day period (from the first to fifth month harvest) is comparable to that observed by others. | train | S0031405612000728-1639 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "months",
"measured_property": null,
"quantity": "7",
"unit": null
},
{
"measured_entity": "Aggregate turnover",
"measured_property": "rates",
"quantity": "range from 4 to 88 days",
"unit": "days"
},
{
"measured_entity": "aggregate stability",
"measured_property": "period",
"quantity": "over a 60 day",
"unit": "day"
},
{
"measured_entity": "aggregate water repellency",
"measured_property": "period",
"quantity": "over a 120 day",
"unit": "day"
}
] |
measeval | Principal component analysis of fungal TRFs for (A) all seven months combined and (B) for month 7 only. | train | S0031405612000728-769 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "months",
"measured_property": null,
"quantity": "seven",
"unit": null
}
] |
measeval | Inspection of the datasets from a large number of orbits showed that it was convenient to locate the Ion Composition Boundary (ICB), which marks the transition between the shocked solar wind and the planetary plasma (e.g. Martinecz et al., 2008), by considering the mass channel number at which the largest number of ions was observed in each 192 s cycle. Data from times at which the mass channel number of the maximum ion count was 15 or less were taken to correspond to altitudes below the ICB. These data were then considered for further analysis. For example, in the data set for 9th August 2008 shown in Fig. 1, the data between 05:28 UT and 05:47 UT were interpreted as being from inside the ICB. These data are shown within the pink box in Fig. 1, and it was these data that were considered for further analysis in this particular example. | train | S0032063312002437-627 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "cycle",
"measured_property": null,
"quantity": "192 s",
"unit": "s"
},
{
"measured_entity": "maximum ion count",
"measured_property": "mass channel number",
"quantity": "15 or less",
"unit": null
},
{
"measured_entity": "data set",
"measured_property": null,
"quantity": "9th August 2008",
"unit": null
},
{
"measured_entity": "data",
"measured_property": null,
"quantity": "between 05:28 UT and 05:47 UT",
"unit": "UT"
}
] |
measeval | The first panel of Fig. 1 shows an example of a peaked electron distribution. The time interval of the ELS spectrogram is 12 min, revealing a clear “inverted-V” structure over a 2-min interval at the centre of the spectrogram. The DEF energy spectrum at the right shows a positive gradient below the energy of the peak, which is at approximately 60 eV. This corresponds to a peak of similar energy when the energy spectrum is plotted in PSD, but is not shown here. By comparing the energy spectrum of the electron feature to those from the different regions around Mars we find the origin is most likely from the solar wind. Comparing to the modelled Maxwellian distributions show the presence of an accelerated peak as well as an added contribution of heated electrons. This indicates the electron feature could be some way between penetrating solar wind and magnetosheath electrons. Evidence of electrons being accelerated and heated has been found in previous analysis of “inverted-V” electrons and is observed as preferably transverse to the magnetic field at low altitudes (Lundin et al., 2006b; Dubinin et al., 2009). | train | S0032063312003054-1990 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "ELS spectrogram",
"measured_property": "time interval",
"quantity": "12 min",
"unit": "min"
},
{
"measured_entity": "clear “inverted-V” structure",
"measured_property": "over",
"quantity": "2-min",
"unit": "min"
},
{
"measured_entity": "peak",
"measured_property": "energy",
"quantity": "approximately 60 eV.",
"unit": "eV"
}
] |
measeval | It is possible to explain such observations in general if we consider MEX to be above a region that has accelerated electrons upwards, in which case heavy-ions could also be accelerated downwards. This may explain the appearance of the very low DEF of heavy-ions at ∼400eV. However, due to the finite gyro-radius effect it is also plausible that the general behaviour of the heavy-ions flowing away from Mars may not change, even when passing an acceleration region. The gyroradius of heavy-ions with energies around ∼10eV at the location of the accelerated electron signature is around 100 km, which is of a similar spatial scale to the horizontal size of a closed crustal magnetic field line at 400 km. Therefore, it is possible the heavy-ions do not remain in the acceleration region long enough to experience its effects. | train | S0032063312003054-2264 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "heavy-ions",
"measured_property": "DEF",
"quantity": "∼400eV.",
"unit": "eV"
},
{
"measured_entity": "heavy-ions",
"measured_property": "energies",
"quantity": "∼10eV",
"unit": "eV"
},
{
"measured_entity": "heavy-ions",
"measured_property": "gyroradius",
"quantity": "around 100 km",
"unit": "km"
},
{
"measured_entity": "closed crustal magnetic field line",
"measured_property": "horizontal size",
"quantity": "400 km",
"unit": "km"
}
] |
measeval | The combination of electron and heavy-ion energy distributions that make up category-2, suggestive of a downward current and category-4 with both up-going electrons and heavy-ions, is only considered for those electron precipitation signatures that have electron energy distributions with a significant asymmetry. For these signatures, up-going electrons and heavy-ions are the most common combination, while the combination of up-going electrons and down-going heavy-ions occur almost half as often. Even after discounting upward net flux of electrons from those signatures with isotropic electron energy distribution, category-4 still make up the second largest group and when added together with category-2 occur on 10% of MEX orbits. | train | S0032063312003054-2467 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "MEX orbits",
"measured_property": "category-4 still make up the second largest group and when added together with category-2",
"quantity": "10%",
"unit": "%"
}
] |
measeval | Fig. 5 also shows an acceleration of heavy-ions between ∼01:25:00 UT and 01:25:40 UT, prior to the first signature of electron precipitation shown in the ELS spectrogram. Similar acceleration of heavy-ions is found around a number of other events of electron precipitation signatures. Further analysis of this type of acceleration of heavy-ions will be left for future work. However, we refer to these events as showing a “peripheral acceleration” of heavy-ions. We have included the identification of these events in Table 1 to compare with the results of the energy distribution categories. | train | S0032063312003054-2483 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "heavy-ions",
"measured_property": "acceleration",
"quantity": "between ∼01:25:00 UT and 01:25:40 UT",
"unit": "UT"
}
] |
measeval | Out of the total 689 events of electron precipitation signatures, 85 were observed with a concurrent acceleration of heavy-ions. This accounts for 12% of precipitation signatures occurring on ∼5% of MEX orbits. Only 37 events of electron precipitation signatures were observed with a peripheral acceleration of heavy-ions. This makes the peripheral acceleration of heavy-ions less common occurring on just 5% of precipitation signatures and on ∼2% of MEX orbits. Therefore, it is quite rare to observe accelerated beams of heavy-ions during signatures of electron precipitation at Mars. | train | S0032063312003054-2501 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "events of electron precipitation signatures",
"measured_property": null,
"quantity": "689",
"unit": null
},
{
"measured_entity": "events of electron precipitation signatures",
"measured_property": null,
"quantity": "85",
"unit": null
},
{
"measured_entity": "precipitation signatures occurring on ∼5% of MEX orbits",
"measured_property": "concurrent acceleration of heavy-ions",
"quantity": "12%",
"unit": "%"
},
{
"measured_entity": "MEX orbits",
"measured_property": null,
"quantity": "∼5%",
"unit": "%"
},
{
"measured_entity": "events of electron precipitation signatures",
"measured_property": null,
"quantity": "37",
"unit": null
},
{
"measured_entity": "precipitation signatures",
"measured_property": "peripheral acceleration of heavy-ions",
"quantity": "5%",
"unit": "%"
},
{
"measured_entity": "MEX orbits",
"measured_property": "peripheral acceleration of heavy-ions",
"quantity": "∼2%",
"unit": "%"
}
] |
measeval | We interpret our FAC density profiles by considering the corresponding precipitating electron energy fluxes, as shown in Fig. 6(b). Fluxes are plotted as functions of latitude. The line style code and labels are the same as in Fig. 6(a), the latitudinal size of a HST ACS-SBC pixel is indicated by the dark grey rectangle and the grey solid line indicates the limit of present HST detectability (~1kR; Cowley et al., 2007). We begin by comparing profiles for case ES with EF, which are almost identical and both have maxima at ~74° latitude, equivalent to the location of the ‘main auroral oval’, and at ~80°, the boundary between open (region I) and closed field lines (region II). Therefore, we would expect a fairly bright auroral oval of ~88kR for case ES and ~79kR for case EF. The electron energy flux for case EF (~7.85mWm−2) is ~10% smaller than case ES (~8.8mWm−2) due to ΩT(ES)>ΩT(EF) leading to a smaller flow shear. Our model also predicts the possibility of observable polar emission (region II/I boundary) of ~15kR for both cases ES and EF. However, this region is strongly dependent on the plasma flow model used and poorly constrained by observations. | train | S0032063313003218-6651 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "limit of present HST detectability",
"measured_property": null,
"quantity": "~1kR",
"unit": "kR"
},
{
"measured_entity": "case ES with EF",
"measured_property": "maxima",
"quantity": "~74° latitude",
"unit": "° latitude"
},
{
"measured_entity": "case ES with EF",
"measured_property": "boundary between open (region I) and closed field lines (region II)",
"quantity": "~80°",
"unit": "°"
},
{
"measured_entity": "case ES",
"measured_property": "auroral oval",
"quantity": "~88kR",
"unit": "kR"
},
{
"measured_entity": "case EF",
"measured_property": "auroral oval",
"quantity": "~79kR",
"unit": "kR"
},
{
"measured_entity": "case EF",
"measured_property": "electron energy flux",
"quantity": "(~7.85mWm−2",
"unit": "mWm−2"
},
{
"measured_entity": "case EF",
"measured_property": "electron energy flux",
"quantity": "~10%",
"unit": null
},
{
"measured_entity": "case ES",
"measured_property": "electron energy flux",
"quantity": "~8.8mWm−2",
"unit": "mWm−2"
},
{
"measured_entity": "model also predicts",
"measured_property": "observable polar emission (region II/I boundary)",
"quantity": "~15kR",
"unit": "kR"
}
] |
measeval | The particles also significantly toughened the epoxy polymer even at about −100 °C. | train | S0032386113005454-2008 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "particles",
"measured_property": "toughened",
"quantity": "about −100 °C",
"unit": "°C"
}
] |
measeval | The glass transition temperature, Tg, of all the bulk samples was measured using dynamic-mechanical thermal analysis (DMTA) with a Q800 DMA from TA Instruments, UK. A double-cantilever mode at 1 Hz was employed using test specimens 60 × 10 × 3 mm3 in size. The temperature range used was −100 °C to 200 °C with a heating rate of 4 °C/min. The value of Tg was determined at the peak value of tan δ. The number average molecular weight between cross-links, Mnc, was also calculated from the equilibrium modulus in the rubbery region, Er, using [20](1)Mnc=qρRT/Erwhere T is the temperature in K at which the value of Er was taken, ρ is the density of the epoxy at the temperature T, the term R is the universal gas constant, and q is the front factor. As the density of the epoxy was only measured at room temperature, the value of the front factor, q, was taken to be 0.725, as in previous work [21]. The density, ρ, of the epoxy was measured at room temperature according to BS ISO 1183-1 Method A [22] to be 1.20 g/m3 at 20 °C. | train | S0032386113005454-2055 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "double-cantilever mode",
"measured_property": null,
"quantity": "1 Hz",
"unit": "Hz"
},
{
"measured_entity": "test specimens",
"measured_property": "size",
"quantity": "60 × 10 × 3 mm3",
"unit": "mm3"
},
{
"measured_entity": "temperature range",
"measured_property": null,
"quantity": "−100 °C to 200 °C",
"unit": "°C"
},
{
"measured_entity": "heating rate",
"measured_property": null,
"quantity": "4 °C/min",
"unit": "°C/min"
},
{
"measured_entity": "epoxy",
"measured_property": null,
"quantity": "room temperature",
"unit": null
},
{
"measured_entity": "front factor",
"measured_property": null,
"quantity": "0.725",
"unit": null
},
{
"measured_entity": "epoxy",
"measured_property": null,
"quantity": "room temperature",
"unit": null
},
{
"measured_entity": "epoxy",
"measured_property": "density",
"quantity": "1.20 g/m3",
"unit": "g/m3"
},
{
"measured_entity": "room temperature",
"measured_property": null,
"quantity": "20 °C",
"unit": "°C"
}
] |
measeval | A tensile modulus of 3.19 ± 0.10 GPa was measured for the unmodified epoxy polymer. The modulus decreased approximately linearly with increasing CSR content to 1.96 ± 0.08 GPa when 20 wt% of S-CSR particles were added, see Table 1. Similar results were reported by Giannakopoulos et al. [30] using the same formulation of epoxy polymer but with different CSR particles. | train | S0032386113005454-2308 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "unmodified epoxy polymer",
"measured_property": "tensile modulus",
"quantity": "3.19 ± 0.10 GPa",
"unit": "GPa"
},
{
"measured_entity": "epoxy polymer",
"measured_property": "modulus",
"quantity": "to 1.96 ± 0.08 GPa",
"unit": "GPa"
},
{
"measured_entity": "S-CSR particles",
"measured_property": "were added",
"quantity": "20 wt%",
"unit": "wt%"
}
] |
measeval | The mean room-temperature values of the compressive true yield stress, σyc, compressive true fracture stress, σfc, and compressive true fracture strain, γf, are summarised in Table 2. The tensile yield stress is calculated from the measured compressive yield stress [39]. The addition of S-CSR particles reduces the compressive true yield stress, as expected, due to the relative softness of the polysiloxane rubber. The values decreased approximately linearly with increasing S-CSR particle content, see Fig. 4. At 20 °C, a value of 111 MPa was measured for the unmodified epoxy polymer, which also reveals that the unmodified epoxy should have the highest strength, if the effect of defects is excluded, when the test is conducted in uniaxial tension. The lowest value of the compressive true yield stress was measured to be 63 MPa for the 20 wt% S-CSR-modified epoxy polymer. | train | S0032386113005454-2601 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "unmodified epoxy polymer",
"measured_property": null,
"quantity": "20 °C",
"unit": "°C"
},
{
"measured_entity": "unmodified epoxy polymer",
"measured_property": null,
"quantity": "111 MPa",
"unit": "MPa"
},
{
"measured_entity": "compressive true yield stress",
"measured_property": "lowest value",
"quantity": "63 MPa",
"unit": "MPa"
},
{
"measured_entity": "S-CSR-modified epoxy polymer",
"measured_property": null,
"quantity": "20 wt%",
"unit": "wt%"
}
] |
measeval | At room temperature, the fracture surfaces of the S-CSR particle-modified polymers also showed crack forking and feather markings. However, these fracture surfaces are rougher than those of the unmodified epoxy, and scanning electron micrographs of the deformation zone for the 10 wt% and 20 wt% S-CSR-modified epoxy polymers are shown in Fig. 13. The fracture surfaces are covered with cavitated S-CSR particles, which can be identified as the circular features in Fig. 13. The cavitation process causes the originally solid rubber particles to deform into a rubbery shell surrounding a void. The mean diameter of these cavities was measured to be 0.296 μm. This is significantly larger than the mean diameter of the S-CSR particles measured from the AFM images, which was 0.18 μm. This observation clearly reveals that plastic void growth of the epoxy polymer has followed cavitation of the S-CSR particles. This is one of the main toughening mechanisms for such thermoset polymers toughened by the presence of well-dispersed rubber particles. Essentially, the cavitation of the particle creates voids which relieve the triaxial stress-state ahead of the crack tip and so enable plastic void growth to occur far more readily in the epoxy polymer. Cavitation, as opposed to particle debonding, will occur when the rubber particle is strongly bonded to the surrounding polymer. Indeed, based on the FEG-SEM observations, the core to shell adhesion must also be relatively high for the S-CSR particles, as no debonding is observed. For the low-temperature results, the fracture surfaces of the particle-modified polymers are very similar to the samples tested at room temperature, see Figs. 14 and 15. Indeed, all of the S-CSR particles cavitated, even at −109 °C, although the size of the cavities is reduced at low temperatures, which indicates a lesser extent of plastic void growth in the epoxy polymer. | train | S0032386113005454-2865 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "S-CSR particle-modified polymers",
"measured_property": null,
"quantity": "room temperature",
"unit": null
},
{
"measured_entity": "S-CSR-modified epoxy polymers",
"measured_property": null,
"quantity": "10 wt% and 20 wt%",
"unit": "%"
},
{
"measured_entity": "cavities",
"measured_property": "mean diameter",
"quantity": "0.296 μm",
"unit": "μm"
},
{
"measured_entity": "S-CSR particles",
"measured_property": "mean diameter",
"quantity": "0.18 μm",
"unit": "μm"
},
{
"measured_entity": "samples",
"measured_property": null,
"quantity": "room temperature",
"unit": null
},
{
"measured_entity": "S-CSR particles",
"measured_property": "cavitated",
"quantity": "−109 °C",
"unit": "°C"
}
] |
measeval | An energy-based criterion was used to predict debonding of the particles. The method used has been fully described elsewhere [49] and essentially it proposes that the criterion for debonding is based upon the energy released by the debonding process. To obtain the parameters needed for this energy-based criterion, a finite-element analysis modelling study has been used to derive the change in strain-energy arising from the cavitation process, with the addition of the strain-energy stored in the particle prior to debonding. The applied stress used for these simulations was derived from experimental observations. Namely, as implied above, the debonding of the silica nanoparticles from the epoxy matrix polymer appears to take place during the elastic deformation region and, as shown in Table 1, the yield stress for all modified epoxy polymers is approximately equal, irrespective of particle size. It has therefore been assumed that the debonding takes place at an applied uniaxial stress of about 70 MPa, which equates to a hydrostatic stress at the crack tip of about 210 MPa. Thus, the finite-element analysis simulations were analysed for an applied hydrostatic stress of 210 MPa. | train | S0032386113009889-2123 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "debonding",
"measured_property": "applied uniaxial stress",
"quantity": "about 70 MPa",
"unit": "MPa"
},
{
"measured_entity": "debonding",
"measured_property": "hydrostatic stress",
"quantity": "about 210 MPa",
"unit": "MPa"
},
{
"measured_entity": "finite-element analysis simulations",
"measured_property": "applied hydrostatic stress",
"quantity": "210 MPa",
"unit": "MPa"
}
] |
measeval | We conclude that the biological components contributing to RS at the forest site of San Rossore were mostly from heterotrophic origin, and constrained within the top 20–30 cm of the soil profile. Our results reflected the soil respiration processes which characterize a water- and nutrient-limited forest sites such as San Rossore (Rosenkranz et al., 2006). In a recent study on the soil organic carbon of six Mediterranean forest sites in Italy, Chiti et al. (2010) reported that the pine forest of San Rossore had the lowest SOC accumulation within the top 20 cm of soil profile, and predicted that by the end of the second commitment period of the Kyoto protocol (2013–2017) would become a source of SOC. | train | S0038071711004354-2573 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "soil profile",
"measured_property": null,
"quantity": "20–30 cm",
"unit": "cm"
},
{
"measured_entity": "Mediterranean forest sites in Italy",
"measured_property": null,
"quantity": "six",
"unit": null
},
{
"measured_entity": "soil profile",
"measured_property": null,
"quantity": "20 cm",
"unit": "cm"
}
] |
measeval | Despite the lack of correlation between aggregate stability and AMF, rapid growth of G. mosseae hyphae suggested by Jansa et al. (2008) might be expected to result in alterations in soil structure relative to hyphae of other slower growing species. Work by Giovannetti et al. (2001, 2004) demonstrated that AMF hyphae frequently anastamose and could result in a large network of interconnecting hyphae. Within the current experiment, colonisation by G. mosseae resulted in a higher proportion of small soil pores to large ones and reduced distances between neighbouring pores. A soil with a greater proportion of small pores (∼0.4 mm2) will retain water more effectively than a soil with larger pores; furthermore, small pores act as nutrient-rich habitats for soil microflora (Nunan et al., 2001). In contrast, G. intraradices led to soil with a greater number of large pores and all combinations of G. intraradices resulted in greater distances between neighbouring pores. Bearden (2001) concluded, in a study of soil–water characteristics, that AMF hyphae resulted in soils gaining groups of small pores which corroborates the current findings, although it is interesting that different Glomus species affect the size and distance between pores differently. Bearden (2001) used a combination of six Glomus species (including G. mosseae) in her investigation of vertisols. In contrast, Milleret et al. (2009) found no evidence of G. intraradices forming small diameter structural pores and the larger size of the pores relative to the AMF led them to conclude that pore development was an indirect effect of the mycorrhiza. Since the extraradical hyphae had a smaller diameter than the pores, the authors suggested that hyphal penetration of pores induced root exudation and increased microbial activity. The data in the current investigation show that plant roots, possibly by influencing the associated microbial biomass and not colonisation by AMF per se, positively affected aggregate stability. Previous studies (e.g. Bedini et al., 2009) showed that aggregate stability is affected by the plant-fungal system rather than by plant root metabolism. Hallett et al. (2009) performed an elegant experiment in which mycorrhizal-deficient and ‘wild type’ tomato plants were grown in an agricultural field soil amended with G. mosseae and G. intraradices. These authors demonstrated that aggregate stability and porosity were increased in planted soils irrespective of the plant type (and mycorrhizal status) and they conclude that roots drive the stabilisation of soil structure. Their experiment lasted for 84 days and during this time the soil experienced wet–dry cycles which may have encouraged microbial populations to produce polysaccharides which in turn enhanced aggregate production. However in the current study, individual AMF species and combinations of co-occurring AMF species caused short-term effects on pore dynamics which may result in long-term impacts on porosity and aggregate stability. Whilst it has been shown before that different AMF species may affect aggregate stability (Schreiner et al., 1997), the focus here is on the effects of the mycelium in addition to the mycorrhiza. No correlations were observed for AMF hyphal abundance and soil physical properties, demonstrating that the differences observed may relate to growth patterns and mycelium architecture rather than density, or perhaps to AMF-specific altered C-exudation since AM fungi are known to produce extracellular soil proteins such as glomalin (Wright and Upadhyaya, 1996). The data presented here demonstrate that different AMF species within the same genus may have contrasting effects on soil pore characteristics which could alter the micro-physical habitat for other soil organisms. | train | S0038071712001010-1044 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "small pores",
"measured_property": null,
"quantity": "∼0.4 mm2",
"unit": "mm2"
},
{
"measured_entity": "experiment",
"measured_property": "lasted",
"quantity": "84 days",
"unit": "days"
}
] |
measeval | AM fungal inoculum as a single factor was significant (P < 0.001) with distinct species combinations resulting in different levels of root colonisation. The individual inocula resulted in similar levels of colonisation (9.9%–16.2%; LSD = 7.01) irrespective of species, therefore valid comparisons with and between the mixed species could be made. The highest percentage colonisation was observed in the two-species mixture of G. geosporum plus G. mosseae (25.4%), followed by the combination of all three species (23.8%). Percentage colonisation by G. geosporum plus G. mosseae in combination appeared to be additive relative to that observed by the species individually (16.2%, G. geosporum; 9.9% G. mosseae). Colonisation was markedly reduced when G. mosseae and G. intraradices were paired (7.5%), particularly compared to the performance of G. intraradices individually (14.2%). Arbuscules followed a similar pattern to hyphal colonisation, with the G. geosporum and G. mosseae combination containing the most arbuscules (data not shown). No mycorrhizal colonisation was observed in the uninoculated plant roots. | train | S0038071712001010-918 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "AM fungal inoculum as a single factor",
"measured_property": "P",
"quantity": "< 0.001",
"unit": null
},
{
"measured_entity": "levels of colonisation",
"measured_property": null,
"quantity": "9.9%–16.2%",
"unit": "%"
},
{
"measured_entity": "levels of colonisation",
"measured_property": "LSD",
"quantity": "7.01",
"unit": null
},
{
"measured_entity": "two-species mixture of G. geosporum plus G. mosseae",
"measured_property": "percentage colonisation",
"quantity": "25.4%",
"unit": "%"
},
{
"measured_entity": "combination of all three species",
"measured_property": "percentage colonisation",
"quantity": "23.8%",
"unit": "%"
},
{
"measured_entity": "G. geosporum",
"measured_property": "Percentage colonisation",
"quantity": "16.2%",
"unit": "%"
},
{
"measured_entity": "G. mosseae",
"measured_property": "Percentage colonisation",
"quantity": "9.9%",
"unit": "%"
},
{
"measured_entity": "G. mosseae and G. intraradices",
"measured_property": "Colonisation",
"quantity": "7.5%",
"unit": "%"
},
{
"measured_entity": "G. intraradices",
"measured_property": "Colonisation",
"quantity": "14.2%",
"unit": "%"
}
] |
measeval | None of the mycorrhizal fungi when inoculated individually increased plant biomass. However, root growth responded positively to the G. mosseae plus G. intraradices combination, resulting in a mycorrhizal growth response of 115% on a whole plant basis and 169% on a root only basis (P = 0.001) (Fig. 2). The fungal combination that resulted in the lowest percentage root length colonised induced the highest mycorrhizal growth response. | train | S0038071712001010-944 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "mycorrhizal growth",
"measured_property": "G. mosseae plus G. intraradices combination",
"quantity": "115%",
"unit": "%"
},
{
"measured_entity": "mycorrhizal growth",
"measured_property": "G. mosseae plus G. intraradices combination",
"quantity": "169%",
"unit": "%"
},
{
"measured_entity": "mycorrhizal growth response",
"measured_property": "P",
"quantity": "0.001",
"unit": null
}
] |
measeval | A conceptual picture on what was a healthy or a diseased soil could be perceived by looking at the responses of majority of fungal community members to soil variables. Majority (55%) of OTUs in healthy soils were stimulated (encouraged) by a certain set of soil variables but the majorities (63%) in diseased soils were inhibited (discouraged) (Table 1). For a complex natural community, it is impossible that every environmental element encourages every community member: majority makes senses. With this view, a healthy soil was likely a soil with variables that encouraged majority of fungal community, whereas a diseased soil was a soil with variables that discouraged majorities. Any society that encourages majority of its members will be more likely to become a vigorous and successful society. | train | S0038071713001971-1388 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "OTUs in healthy soils",
"measured_property": "stimulated (encouraged) by a certain set of soil variables",
"quantity": "55%",
"unit": "%"
},
{
"measured_entity": "OTUs",
"measured_property": "were inhibited (discouraged)",
"quantity": "63%",
"unit": "%"
}
] |
measeval | The following are the supplementary data related to this article:Fig. S1The yield records of recent two years. The yield level between dashed lines indicated the yield range of local farmers who practice rotations. | train | S0038071713001971-1427 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "yield records",
"measured_property": null,
"quantity": "two years",
"unit": "years"
}
] |
measeval | As an example, the open source calculator bc contains 9438 lines of code represented by 7538 SDG vertices. The B-MSG for bc, shown in Fig. 3a, contains a large plateau that spans almost 70% of the MSG. Under the assumption that same slice size implies the same slice, this indicates a large same-slice cluster. However, “zooming” in reveals that the cluster is actually composed of several smaller clusters made from slices of very similar size. The tolerance implicit in the visual resolution used to plot the MSG obscures this detail. | train | S016412121300188X-4069 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "open source calculator bc",
"measured_property": "contains",
"quantity": "9438 lines of code",
"unit": "lines of code"
},
{
"measured_entity": "9438 lines of code",
"measured_property": "represented",
"quantity": "7538 SDG vertices",
"unit": "SDG vertices"
},
{
"measured_entity": "MSG",
"measured_property": "large plateau",
"quantity": "almost 70%",
"unit": "%"
}
] |
measeval | The accuracy of hash function H is given as Hashed Slice Precision, HSP = UH/US . A precision of 1.00 (US = UH) means the hash function is 100% accurate (i.e., it produces a unique hash value for every distinct slice) whereas a precision of 1/US means that the hash function produces the same hash value for every slice leaving UH = 1. | train | S016412121300188X-4392 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "hash function",
"measured_property": "precision",
"quantity": "1.00",
"unit": null
},
{
"measured_entity": "hash function",
"measured_property": "accurate",
"quantity": "100%",
"unit": "%"
},
{
"measured_entity": "hash function",
"measured_property": "precision",
"quantity": "1/",
"unit": null
},
{
"measured_entity": "UH",
"measured_property": null,
"quantity": "1",
"unit": null
}
] |
measeval | To assess if a program includes a large coherent cluster, requires making a judgement concerning what threshold constitutes large. Following prior empirical work (Binkley and Harman, 2005; Harman et al., 2009; Islam et al., 2010a,b), a threshold of 10% is used. In other words, a program is said to contain a large coherent cluster if 10% of the program's SDG vertices produce the same backward slice as well as the same forward slice. | train | S016412121300188X-4436 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "threshold",
"measured_property": null,
"quantity": "10%",
"unit": "%"
},
{
"measured_entity": "program's SDG vertices",
"measured_property": "produce the same backward slice as well as the same forward slice",
"quantity": "10%",
"unit": "%"
}
] |
measeval | Table 4 shows the statistics for the five largest clusters of acct. Column 1 gives the cluster number, where 1 is the largest and 5 is the 5th largest cluster measured using the number of vertices. Columns 2 and 3 show the size of the cluster as a percentage of the program's vertices and actual vertex count, as well as the line count. Columns 4 and 5 show the number of files and functions where the cluster is found. The cluster sizes range from 11.4% to 2.4%. These five clusters can be readily identified in the Heat-Map visualization (not shown) of decluvi. The rest of the clusters are very small (less than 2% or 30 vertices) in size and are thus of little interest. | train | S016412121300188X-4545 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "largest clusters of acct",
"measured_property": null,
"quantity": "five",
"unit": null
},
{
"measured_entity": "cluster",
"measured_property": "sizes",
"quantity": "range from 11.4% to 2.4%",
"unit": "%"
},
{
"measured_entity": "clusters",
"measured_property": null,
"quantity": "five",
"unit": null
},
{
"measured_entity": "rest of the clusters",
"measured_property": null,
"quantity": "less than 2%",
"unit": "%"
},
{
"measured_entity": "rest of the clusters",
"measured_property": null,
"quantity": "30 vertices",
"unit": "vertices"
}
] |
measeval | The next case study uses indent to further support the answer found for RQ4 in the acct case study. The characteristics of indent are very different from those of acct as indent has a very large dominant coherent cluster (52%) whereas acct has multiple smaller clusters with the largest being 11%. We include indent as a case study to ensure that the answer for RQ4 is derived from programs with different cluster profiles and sizes giving confidence as to the generality of the answer. | train | S016412121300188X-4617 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "very large dominant coherent cluster",
"measured_property": null,
"quantity": "52%",
"unit": "%"
},
{
"measured_entity": "largest",
"measured_property": null,
"quantity": "11%",
"unit": "%"
}
] |
measeval | Indent has one extremely large coherent cluster that spans 52.1% of the program's vertices. The cluster is formed of vertices from 54 functions spread over 7 source files. This cluster captures most of the logical functionalities of the program. Out of the 54 functions, 26 begin with the common prefix of “handle_token”. These 26 functions are individually responsible for handling a specific token during the formatting process. For example, handle_token_colon, handle_token_comma, handle_token_comment, and handle_token_lbrace are responsible for handling the colon, comma, comment, and left brace tokens, respectively. | train | S016412121300188X-4640 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "coherent cluster",
"measured_property": null,
"quantity": "one",
"unit": null
},
{
"measured_entity": "program's vertices",
"measured_property": "coherent cluster",
"quantity": "52.1%",
"unit": "%"
},
{
"measured_entity": "functions",
"measured_property": null,
"quantity": "54",
"unit": null
},
{
"measured_entity": "source files",
"measured_property": null,
"quantity": "7",
"unit": null
},
{
"measured_entity": "functions",
"measured_property": null,
"quantity": "54",
"unit": null
},
{
"measured_entity": "functions",
"measured_property": null,
"quantity": "26",
"unit": null
},
{
"measured_entity": "functions",
"measured_property": null,
"quantity": "26",
"unit": null
}
] |
measeval | As coherent clusters are composed of both backward and forward slices, the stability of the backward slice profile itself does not guarantee the stability of coherent cluster profile. The remainder of this section looks at how the clustering profile is affected by bug fixes. Fig. 20 shows individual SCGs for each version of barcode. As coherent clusters are dependent on both backward and forward slices, such clusters will be more sensitive to changes in dependences within the program. The SCGs show that from the initial version barcode-0.90 there were two coherent clusters in the system. The smaller one is around 10% of the code while the larger is around 40% of the code. As the system evolved and went through various modifications and enhancements, the number of clusters and the profile of the clusters remained consistent other than its scaled growth with the increase in program size. It is also evident that during evolution of the system, the enhancement code or newly added code formed part of the larger cluster. This is why in the later stages of the evolution we see an increase in the size of the largest cluster, but not the smaller one. | train | S016412121300188X-4937 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "coherent clusters",
"measured_property": null,
"quantity": "two",
"unit": null
},
{
"measured_entity": "code",
"measured_property": "coherent clusters",
"quantity": "around 10%",
"unit": "%"
},
{
"measured_entity": "code",
"measured_property": "coherent clusters",
"quantity": "around 40%",
"unit": "%"
}
] |
measeval | As an answer to RQ7, this study finds that unless there is significant refactoring of the system, coherent cluster profiles remain stable during system evolution and thus captures the core architecture of the program in all three case studies. Future work will replicate this longitudinal study on a large code corpus to ascertain whether this stability holds for other programs. | train | S016412121300188X-5038 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "case studies",
"measured_property": null,
"quantity": "three",
"unit": null
}
] |
measeval | This paper extends our previous work which introduced coherent dependence clusters (Islam et al., 2010b) and decluvi (Islam et al., 2010a). Previous work established the existence of coherent dependence clusters and detailed the functionalities of the visualization tool. This paper extends previous work in many ways, firstly by introducing an efficient hashing algorithm for slice approximation. This improves on the precision of previous slice approximation from 78% to 95%, resulting in precise and accurate clustering. The coherent cluster existence study is extended to empirically validate the results by considering 30 production programs. Additional case studies show that coherent clusters can help reveal the structure of a program and identify structural defects. We also introduce the notion of inter-cluster dependence which will form the base of reverse engineering efforts in future. Finally, we also present studies which show the lack of correlation between coherent clusters and bug fixes and show that coherent clusters remain surprisingly stable during system evolution. | train | S016412121300188X-5066 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "precision of previous slice",
"measured_property": "improves",
"quantity": "from 78% to 95%",
"unit": "%"
},
{
"measured_entity": "production programs",
"measured_property": null,
"quantity": "30",
"unit": null
}
] |
measeval | S. pneumoniae, M. catarrhalis and H. influenzae are the most common pathogens implicated in OME, and all are capable of forming biofilms [33,42]. However, rather than focusing just on those three bacteria, this study cultured effusions on a wide range of different media for prolonged time periods in order to capture as many isolates as possible. Interestingly, coagulase negative staphylococci (CoNS), Veillonella spp. and S. aureus were the three commonest pathogens isolated in this study. CoNS were long thought to be non-pathogenic commensals, but with the recognition of their biofilm-forming capacity have emerged as the leading cause of biomaterials-related infection [43,43]. S. lugdunensis, isolated here on three occasions, in particular has been implicated in endocarditis, wound infection, and implant-related infection as well as otitis media, behaving more like S. aureus than other CoNS [45]. Other CoNS have also been previously implicated in otitis media, with a recent study finding that they account for 60% of bacteria isolated from OME [46,47]. Veillonella is a Gram-negative anaerobe that inhabits the mouth and upper respiratory tract, forms biofilms [48] and has previously been found in middle ear disease [49,50]. S. aureus also forms biofilms and has been identified in middle ear disease [51,52]. Although most of the bacteria in Table 2 have previously been isolated in middle ear disease, to the best of the authors’ knowledge Flavimonas oryzihabitans, Vibrio metschnikovii and Gemella haemolysans have not been implicated previously. | train | S0165587612003680-1078 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "bacteria",
"measured_property": null,
"quantity": "three",
"unit": null
},
{
"measured_entity": "commonest pathogens",
"measured_property": null,
"quantity": "three",
"unit": null
},
{
"measured_entity": "S. lugdunensis",
"measured_property": "isolated",
"quantity": "three occasions",
"unit": "occasions"
},
{
"measured_entity": "bacteria isolated from OME",
"measured_property": "CoNS",
"quantity": "60%",
"unit": "%"
}
] |
measeval | A possible explanation for the discrepancy between high PCR-positive rate and low culture-positive rate in OME is the involvement of biofilms in the progression of this pathology [20]. Indeed, biofilms have been identified on human middle ear mucosa in children with OME and/or recurrent AOM in more than 90% of cases, but not in any control samples studied [12]. In addition to tissue surfaces, biofilms have also been identified attached to mucus [21,22] and attach in vitro to collagen gel matrix [23]. In OME, biofilms may be attached to mucus as well as mucosa, thus providing the inflammatory stimulus leading to a middle ear effusion [10,13,24]. | train | S0165587612003680-953 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "children with OME and/or recurrent AOM",
"measured_property": "biofilms have been identified on human middle ear mucosa",
"quantity": "more than 90%",
"unit": "%"
}
] |
measeval | Differences between adults and children were explored on a per-patient (rather than per-ear) basis; where data existed for two ears, the per-patient analysis was carried out using the criteria of at least one ear being culture/confocal positive and at least one ear containing biofilms. Children appeared to have a greater number of culture-positive, confocal-positive, and biofilm results than adults (54.3% vs. 14.3%, 82.9% vs. 57.1%, and 67.9% vs. 0%, respectively). However, only the presence of biofilms reached statistical significance (Fisher's exact test p = 0.02). | train | S0165587612003680-998 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "ears",
"measured_property": null,
"quantity": "two",
"unit": null
},
{
"measured_entity": "ear",
"measured_property": null,
"quantity": "at least one",
"unit": null
},
{
"measured_entity": "ear",
"measured_property": null,
"quantity": "at least one",
"unit": null
},
{
"measured_entity": "Children",
"measured_property": "culture-positive",
"quantity": "54.3%",
"unit": "%"
},
{
"measured_entity": "adults",
"measured_property": "culture-positive",
"quantity": "14.3%",
"unit": "%"
},
{
"measured_entity": "Children",
"measured_property": "confocal-positive",
"quantity": "82.9%",
"unit": "%"
},
{
"measured_entity": "adults",
"measured_property": "confocal-positive",
"quantity": "57.1%",
"unit": "%"
},
{
"measured_entity": "Children",
"measured_property": "biofilm",
"quantity": "67.9%",
"unit": "%"
},
{
"measured_entity": "adults",
"measured_property": "biofilm",
"quantity": "0%",
"unit": "%"
},
{
"measured_entity": "Fisher's exact test",
"measured_property": "p",
"quantity": "0.02",
"unit": null
}
] |
measeval | FTIR spectra of (a) PLGA fibres, (b) HA nanopowder, (c) PLGA–HA composite fibres, and (d) sintered PLGA–HA fibres. The pure PLGA spectrum shows the C3O characteristic bands in the region 1065–1280 cm−1. The spectrum of HA nanopowder reveals the characteristic peak assigned to PO43−: ν1 vibration mode at about 964 cm−1, ν3 vibration mode at 1031 cm−1 and 1091 cm−1 (asymmetric) respectively. The spectrum of untreated PLGA–HA 50% displays both characteristics of PLGA and HA. However the spectrum of CaP fibres shows the loss of the characteristics of PLGA (C3O bands), suggesting the successful thermal degradation of the polymer, while the characteristics of HA (PO43−) remain present. | train | S0167577X13006393-399 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "pure PLGA spectrum",
"measured_property": "C3O characteristic bands",
"quantity": "1065–1280 cm−1",
"unit": "cm−1"
},
{
"measured_entity": "spectrum of HA nanopowder",
"measured_property": "ν1 vibration mode",
"quantity": "about 964 cm−1",
"unit": "cm−1"
},
{
"measured_entity": "spectrum of HA nanopowder",
"measured_property": "ν3 vibration mode",
"quantity": "1031 cm−1",
"unit": "cm−1"
},
{
"measured_entity": "spectrum of HA nanopowder",
"measured_property": "ν3 vibration mode",
"quantity": "1091 cm−1",
"unit": "cm−1"
},
{
"measured_entity": "PLGA–HA",
"measured_property": null,
"quantity": "50%",
"unit": null
}
] |
measeval | Sintering of pure HA particles is usually reported to occur above 1000 °C. The choice of the sintering temperature is important as it has an effect on the properties of the resulting sample. Most investigators agree that pure HA (ratio CaP=1.67) is stable in an air and argon atmosphere at temperatures upto 1200 °C [18–20]. However, decomposition of HA at temperatures as low as 800 °C has been observed for calcium deficient HA samples [19]. | train | S0167577X13006393-644 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "Sintering of pure HA particles",
"measured_property": "reported to occur",
"quantity": "above 1000 °C",
"unit": "°C"
},
{
"measured_entity": "CaP",
"measured_property": "ratio",
"quantity": "1.67",
"unit": null
},
{
"measured_entity": "pure HA (ratio CaP=1.67) is stable in an air and argon atmosphere",
"measured_property": "temperatures",
"quantity": "upto 1200 °C",
"unit": "°C"
},
{
"measured_entity": "decomposition of HA",
"measured_property": "at temperatures",
"quantity": "low as 800 °C",
"unit": "°C"
}
] |
measeval | The XPS data clearly indicates that the HA nanoparticles used in this experiment are deficient in calcium (Ca/P ratio=1.37). This could explain why the HA decomposition occurs before 1200 °C since deficient HA start their decomposition at temperatures lower than pure HA (ratio=1.67) [19]. In the literature, this decomposition is described as partial although the reason remains unknown. The commonly accepted decomposition reaction for deficient HA isCa10(PO4)6(OH)2→3Ca3(PO4)2+CaO+H2O | train | S0167577X13006393-787 |
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
| [
{
"measured_entity": "Ca/P",
"measured_property": "ratio",
"quantity": "1.37",
"unit": null
},
{
"measured_entity": "HA decomposition",
"measured_property": "occurs",
"quantity": "before 1200 °C",
"unit": "°C"
},
{
"measured_entity": "deficient HA start their decomposition at temperatures lower than pure HA",
"measured_property": "ratio",
"quantity": "1.67",
"unit": null
}
] |
End of preview. Expand
in Dataset Viewer.
A Multi-Domain Corpus for Measurement Extraction (Seq2Seq variant)
A detailed description of corpus creation can be found here.
This dataset contains the training and validation and test data for each of the three datasets measeval, bm, and msp. The measeval, and msp datasets were adapted from the MeasEval (Harper et al., 2021) and the Material Synthesis Procedual (Mysore et al., 2019) corpus respectively.
This repository aggregates extraction to paragraph-level for msp and measeval. Labels are given in json-format as preparation for seq2seq training.
How to load
from datasets import load_dataset
# Only train, all domains
train_dataset = load_dataset("liy140/multidomain-measextract-corpus", "all", split="train")
# All measeval data
measeval_dataset = load_dataset("liy140/multidomain-measextract-corpus", "measeval", split=["train", "val", "test"])
Create Seq2Seq samples
One standard instruction is given, such that such a prompt can be generated by merging text and extraction columns:
### Instruction
You are an expert at extracting quantity, units and their related context from text.
Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.
### Paragraph
The H/H+ transition in the MC09 model occurs near 1.4Rp. If we replace the gray approximation with the full solar spectrum in this model, the H/H+ transition moves higher to 2–3Rp. This is because photons with different energies penetrate to different depths in the atmosphere, extending the heating profile in altitude around the heating peak. This is why the temperature at the 30 nbar level in the C2 model is 3800 K and not 1000 K. In order to test the effect of higher temperatures in the lower thermosphere, we extended the MC09 model to p0 = 1 μbar (with T0 = 1300 K) and again used the full solar spectrum for heating and ionization. With these conditions, the H/H+ transition moves up to 3.4Rp, in agreement with the C2 model. We conclude that the unrealistic boundary conditions and the gray approximation adopted by Murray-Clay et al. (2009) and Guo (2011) lead to an underestimated overall density of H and an overestimated ion fraction. Thus their density profiles yield a H Lyman α transit depth of the order of 2–3% i.e., not significantly higher than the visible transit depth.
### Extractions
[
{
"docId": "S0019103513005058-3154",
"measured_entity": "Soluble sulfate",
"measured_property": null,
"quantity": "1.3 \u00b1 0.5 wt.%",
"unit": "wt.%"
},
{
"docId": "S0019103513005058-3154",
"measured_entity": "soil",
"measured_property": "perchlorate (ClO4-)",
"quantity": "\u223c0.5 wt.%",
"unit": "wt.%"
},
{
"docId": "S0019103513005058-3154",
"measured_entity": "perchlorate-sensitive electrode",
"measured_property": "sensitive to nitrate",
"quantity": "1000 times",
"unit": "times"
},
{
"docId": "S0019103513005058-3154",
"measured_entity": "Viking 1 and Viking 2 landing sites",
"measured_property": "perchlorate",
"quantity": "\u2a7d1.6%",
"unit": "%"
},
{
"docId": "S0019103513005058-3154",
"measured_entity": "martian meteorite EETA79001",
"measured_property": "Native perchlorate",
"quantity": "<1 ppm by mass",
"unit": "ppm by mass"
}
]
Citation
@inproceedings{li-etal-2023-multi-source,
title = "Multi-Source (Pre-)Training for Cross-Domain Measurement, Unit and Context Extraction",
author = "Li, Yueling and
Martschat, Sebastian and
Ponzetto, Simone Paolo",
booktitle = "The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.bionlp-1.1",
pages = "1--25",
abstract = "We present a cross-domain approach for automated measurement and context extraction based on pre-trained language models. We construct a multi-source, multi-domain corpus and train an end-to-end extraction pipeline. We then apply multi-source task-adaptive pre-training and fine-tuning to benchmark the cross-domain generalization capability of our model. Further, we conceptualize and apply a task-specific error analysis and derive insights for future work. Our results suggest that multi-source training leads to the best overall results, while single-source training yields the best results for the respective individual domain. While our setup is successful at extracting quantity values and units, more research is needed to improve the extraction of contextual entities. We make the cross-domain corpus used in this work available online.",
}
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