As a further step, we used the Area Under the Curve (AUC) [24,25] metrics to identify the best configuration of parameters for each machine learning algorithm.
Sample text:```In this study, we generalized previous results regarding the quasi-Gaussian shape and the scalar property using the SBF model [13,21,52].```
Sample intent: result
Sample text:```In this study, we generalized previous results regarding the quasi-Gaussian shape and the scalar property using the SBF model [13,21,52].```
Sample intent: result



Sample text:```The area under the curve represents the probability that the logistic regression model will rank a randomly chosen positive instance (i.e., an error) higher than a randomly chosen negative instance (i.e., nonerror; Fawcett, 2006; Macmillan & Creelman, 2005).```
Sample intent: method



Sample text:```To train our CNN for classification, we set initial learning rate to 0.01 and optimize cross-entropy loss with Adagrad [26].```
Sample intent: background



Sample text:```The results for veridical recall replicated previous results in the literature (Bal-
lardini et al., 2008; McDermott & Watson, 2001), with the average recall of studied words increas-
ing monotonically as SOA increased.```
Sample intent: result



Sample text:```We use BLEU (up to 4-grams) (Papineni et al., 2002) as evaluation metric, which computes the precision of n-grams in generated summaries with gold-standard abstracts as the reference.```
Sample intent: background
Sample text:```The merging is based on the increase in the weighted-bycounts entropy [1] and is stopped by a threshold indicating the minimum number of training samples required to estimate a weight distribution.```
Sample intent: method



Sample text:```5 concentrations were computed for each participant using a hierarchical spatiotemporal model fit with a pragmatic estimation procedure described elsewhere [30,31].```
Sample intent: background



Sample text:```The items are internally consistent and reproducible and the questionnaire may therefore be considered to have at least as favourable levels of reliability as clinical scores used to assess outcomes (Koran 1975).```
Sample intent: result



Sample text:```Only one published paper (Enock et al. 2014) has examined this question, and thatwas in the context of anABM training study.```
Sample intent: method



Sample text:```We use BLEU (up to 4-grams) (Papineni et al., 2002) as evaluation metric, which computes the precision of n-grams in generated summaries with gold-standard abstracts as the reference.```
Sample intent: background



Sample text:```In this study, we generalized previous results regarding the quasi-Gaussian shape and the scalar property using the SBF model [13,21,52].```
Sample intent: result



Sample text:```The above estimator generalizes the estimator in Sriperumbudur et al., 2017 to conditional densities.```
Sample intent: method



Sample text:```Effects of CACC inhibitors were analyzed by the following four-parameter logistic function: y min (max min)/[1 (x/IC50) Hill slope], where min and max are the bottom and top of the curve, respectively, IC50 is the concentration of half inhibition of block, and the Hill slope is a reflection of the steepness of the curve.```
Sample intent: background



Sample text:```rural, we calculated the relative prevalence of a missing value by other demographic and fitness variables using logistic regression [49].```
Sample intent: background



Sample text:```Our heritability result is generally in agreement with the studies of Kien et al. (2008) in Eucalyptus urophylla and Zhao et al. (2014a) in poplar.```
Sample intent: result