<commit_before>

    <b> Dr. <NAME> </b>
    Department of Meteorology
    University of Reading,
    Reading
    UK

    <b> 4<sup>th</sup> March 2016 </b>
<!--
<h2> stuff to read up on </h2>
<li> event attribution studies </li>
<li> DePreSys3/GloSea5 </li>
-->
<!--
<li> climate attribution </li>
<li> aerosols sources and impacts </li>
<li> hindcaste </li>
<li> Dr <NAME>; Dr <NAME>; Prof <NAME> </li>
-->
Dear Dr. <NAME>, Dr <NAME> and Prof <NAME>,

My name is <NAME>, and I would like to be considered for position the “Research Scientist” as part of LOTUS and DYVA projects exploring variability and changes in East Asian Climate. I have recently completed a PhD in climate change, where I utilised experimental and satellite data to developed and constrain a complex numerical vegetation model <citep>Kelley2014a</citep>. I used this model to explore the impacts of future climate change and climate extremes in Australia, including changes in fire <citep>Kelley2014b; Harrison</citep>, drought and hydrology <citep>Zeppel2014; Ukkola</citep>.

Many of these predicted impacts have important consequences for mitigation <citep>Kelley2014b</citep> and adaptation <citep>Ukkola; Harrison</citep> policies. However, these impact "forcasts" still remain largely qualitative due uncertainties in future climate variability and their impact on localised weather extremes (e.g. <cite>Friedlingstein2006; robson2010understanding; Harrison</cite>). Reducing near-term uncertainty (i.e seasonal, inter-annual and decadal) would be a huge step forward in qualitative prediction of extreme events, providing more accurate "early warning" seasonal and inter-annual forecasts which would aid preparation for potential impacts. Reducing this uncertainity requires attribution of extreme events between influences from different driver including anthropogenic forcing, natural climate variability, or indeed the interplay between the two - an extremely complex and difficult task <citep>hartmann2013observations</citep>.

Several new climate model tools, such as GloSea5 (seasonal <cite>QJ:QJ2396</cite>) and DePreSys (inter-annual and decadal, <cite>smith2007objective</cite>), exploit low-frequency ocean oscillations, predictable for years to decades in advance <citep> boer2000study; boer2004long; boer2008multi; robson2010understanding</citep> to reduce uncertainty in inherent climate variability - the most important aspect of uncertainty for near-term predictions <citep>hawkins2009potential</citep>. Recent hindcasts experiments during specific ocean warming events using these tools has shown promising results for near-term predictability of terrestrial climate variability (e.g. <cite> Liu2012b; Robson2013; Knight2014</cite> for DePreSys), and would be ideal for exploring teleconnections between preceding ocean variability and extreme events. The East Asian Summer Monsoon (EASM) is a great example of where these tools could be used. Weakening of the EASM in recent decades - responsible for flooding in southern Asia and droughts in the North - has been attributed to a mix of drivers including global change from anthropogenic greenhouse gas emission <citep>hartmann2013observations</citep>, increases in aerosol emission associated from pollution and land use change <citep>Kaufman2002; Hsu2006; GRL:GRL22181; lau2006asian</citep> and changes in sea surface temperatures (SSTs), associated with the Pacific Decadal Variability and Atlantic Multidecadal Oscillation <citep>Chang2000; hartmann2013observations</citep>.
<NewPage>
Teasing apart the contribution of each driver, and the infractions between drivers, of East Asia extreme weather will be in itself fascinating, and I would relish the opportunity to apply the experience I gained from studying climate impacts in Australia to the challenge. Also, any improvement in our predictive understanding of the monsoonal system will directly benefit adaptation policies that could help the 1/3 of the worlds population that rely on monsoonal rain for their water security, a very worthwhile social endevour.
<!--
I believe that CSSP China programm is the ideal scheme for this – drawing on the strong meterological expertise from across the country, and regional experts at partnership institutes in China, will be vita

will call on some of ??regions experts??, and will help ensure research is directly applicable and relevant to the people the programme is designed to help. In short, the potential value of your research in terms of future environmental and political/social impacts cannot be overstated and it is for this reason that I would very much like to be a part of your team.
-->
Below, I have outlined how my experience in climate research makes me an ideal candidate for the role, with reference to the job description and selection criteria. Further details are also provided in my attached CV.


<!--<li> Knowledge of, or evidence of serious interest in, climate variability and climate change </li>
<li> <i> Knowledge of East Asian climate and monsoon dynamics </i> </li>
<li> <i> Knowledge of ocean-atmosphere interactions and teleconnections </i> </li>
<li> <b> Knowledge of climate event attribution </b> </li>
<li> Research in physical science; if not climate-related, evidence of the ability to acquire the relevant knowledge </li>
<li> Research in climate, ocean, atmosphere or Earth system modelling </li>
<li> Research in climate prediction </li>

<li> <citep> Kelley2014 </citep>: climate response detection
-->
<h2> Climate Science Research Portfolio </h2>
I completed my PhD in Climate Change Ecology just over a year ago. My thesis (“Modelling Australian Fire Regimes”, <cite>kelley2014modelling</cite>), and subsequent postdoctoral research, focussed on climate dynamics in Australia, exploring climate controls on vegetation productivity and vegetation-fire interactions. It used satellite and ground-based observations of weather, climate and vegetation properties to develop and evaluate complex numerical vegetation and land surface models <citep>Prentice2011; Kelley2014a; <NAME></citep>. I have driven some of these models with historical/reanalysis climate data to assess how teleconnections between ENSO and climate affect fire <citep>Kelley2008; Prentice2011</citep>. I drove the model I developed as part of my thesis – the LPX-Mv1 vegetation model <citep>Kelley2014a</citep> - with 21st century CMIP5 climate model outputs to predict climate response of ecosystem resilience and impact on the carbon cycle <citep>Kelley2014b</citep>, fire-dynamics <citep>Harrisonc</citep> and hydrology <citep>Ukkola</citep> in future climate. My thesis demonstrated the importance of incorporating climate-vegetation-fire interactions into present and future climate impact assessments, with my improved dry-climate vegetation simulations altering the magnitude, and in some cases the sign, of predicted future vegetation resilience and water availability. This included counterintuitive but important results that policymakers need to take into account, such as: CO2-related increases in vegetation may have added pressure on available water supply in already dry and drying regions <citep>Ukkola</citep>; and that changes in the likelihood of extreme fire events in dependent on the complex interactions between climate, vegetation compoistion/productivity and extreme whether events - not just extreme events by themselves <citep>Kelley2014b; Harrisonc</citep>.

My examiners consistently stated my thesis fell into the “top 10%” they've ever assessed. I completed my PhD just over one year ago, and I have obtained 7 peer-reviewed publications  – 3 of which are first authored – with a further 3 submitted and 3 near submission. One of these papers is in Nature Geoscience, and all others are in high-impact journals for their field.

In the dissertation for my MSc in Earth Systems Science, I incorporated a fire model into CCDAS -  a data assimilation system used to constrain physical based parameters for use in an Earth System Model framework -  using land-surface satellite observations <citep>Kelley2008; kaminski2013bethy</citep>. During my masters, I was also able to gain experience working with the GENIE Earth System Model, and I wrote a simple box-climate model. I am also currently coding a simple atmospheric transport model as part of a inter-model evaluation and benchmarking project (<a href = "http://douglask3.github.io/firemip.html" target = "_blank">http://douglask3.github.io/fireMIP</a>)

<!--
<li> Very good computing skills. </li>
<li> Programming in Fortran/IDL/Python/<b>Unix shell </b> </li>
<li> <b> Design, execution and analysis of climate model simulations </b> </li>
<li> Substantial experience in computer programming and data analysis </li>
<li> Experience of numerical or climate modelling </li>
<li> Analysis of climate model and observational data </li>
-->
<NewPage>
<h2> Programming and data analysis skills </h2>
The LPX model, that I have been the lead developer on for 6 years, combines FORTRAN (for the scientific component) and C++ (for the I/O component) and can be driven by large-scale climate datasets in either NetCDF or CSV format. I have also used and developed simple vegetation models, as well as a simple climate box-model as part of my masters, in Python, R and Matlab <citep>Kelley2013; Kelley; Whitley</citep>, and have performed statistical analysis and plotting in Python, R, Matlab and, during my masters, in IDL. I have developed some of my R-code into software packages, now available for anyone to download and use (e.g. <cite>Kelley2015; Whitley2016</cite> – both available from http://douglask3.github.io/tools), one of which has recently been adopted and applied to a new model inter-comparison project (fireMIP: <cite>Hantson2016; Hantson</cite>).

My work on developing and evaluating vegetation models has involved processing a range of climate/GIS data products, from raw and processed satellite data (see e.g. <cite>Kelley2008; Prentice2011; Kelley2013; Kelley2014a</cite> & <cite>Kelley2014b</cite>); vector ground observation (<cite>Kelley2013; Kelley2014a; kelley2014modelling</cite>); historical climate and climate reanalysis data <citep>Prentice2011; Kelley2014a; kelley2014modelling</citep>; and climate model data for past <citep>Ciais2011</citep> and future climates <citep>Kelley2014b; Harrison</citep>.

<!--
<li> Communicative and open to collaboration </li>
<li> Willingness to under-take visits to relevant institutions/events </li>
<li> Ability to maintain productive working relationships with collaborators at Reading and other institutions </li>
-->
<h2>Projects and collaborations</h2>
I have been involved in several inter-university and interdisciplinary collaborations during my PhD. I led the design of a vegetation model benchmarking system  <citep>Kelley2013</citep> which  involved liaising with remote sensing experts, botanists, statisticians and other modellers from three different institutions. This system has been widely adopted throughout the vegetation modelling community and is used by several other modelling groups. I have also worked on three vegetation disturbance-trait databases, two of which I was a lead collaborator on <citep>Kelley2014a; kelley2014modelling; harrisonusing; Harrison</citep>.

Outside of my academic work, I was also involved in an international consultancy programme, preforming climate change risk analysis for the Royal Society for the Conservation of Nature (RSCN) in Jordan. Modelling work I conducted for this project focussed on the impacts of predicted short and long term changes in precipitation and rising temperature on ecosystem resilience and river flow, and was used by RSCN to determine areas of key ecological importance and relative stability suitable for nature reserves <citep>harrison2010impacts</citep>.
<!-- <li> Good communication skills both written and oral </li>
<li> Self-motivated, <b>conscientious and creative </b> </li> -->

<h2> Communication </h2>
During my PhD, I was given the opportunity to hone my presentation skills at many group and department seminars (see CV for a full list of presentations). As a result, I was awarded “best presentation” at the departments annual post-graduate conference, out of 78 other presenting students, and was awarded a travel grant to present results of simulations of future ecosystem resilience and carbon stocks at the AGU fall meeting <citep>kelley2013implications</citep>.

I have also attended several workshops, where I have given oral and poster presentations to audiences from a wide variety of disciplines (e.g. <cite>Kelley2008a; Kelleya; Kelley2013a</cite>).
<NewPage>
In my own time, I designed my running clubs website (<a href = "http://www.eppingdac.com.au" target = "_blank">www.eppingdac.com.au</a>), published the clubs newsletter for two years, and was in charge of the clubs publicity for three, during which club membership increased by nearly 50%.

<!--<li> Enjoy working with others </li>-->
<h2> Department and Research Group involvement </h2>
During my PhD I acted as chair for my research groups' paper and research discussion meetings. I was also an informal mentor for several students in the group who were new to statistical programming, including a student who started their PhD as the result of a knowledge exchange initiative from the conclusion of the consultancy work conducted with the RSCN in Jordon, outlined above. This student has recently completed and passed their PhD. I also volunteered with the department's secondary school outreach programme, performing research demonstrations on school open days.

Whilst working as a research assistant at Bristol, I was involved in the teaching of the MSc Earth Systems Science masters programme, giving seminars on causes of, and controls on, wildfire, as well as running modelling workshops on programming for beginners.



Thank you for taking the time to review my application. I hope you agree that my climate research and numerical modelling experience, along with my publishing record and presentation skills, make me ideally suited to the Postdoctoral Researcher role.


Kind regards,

<NAME>


<h2> References </h2>
<References> ReadingMetChina/refList.bib <NewPage>Hantson S. , Harrison S.</NewPage><NewPage>M</NewPage></References><commit_msg>readthrouygh changes and slight reording of statement<commit_after>

    <b> Dr. <NAME> </b>
    Department of Meteorology
    University of Reading,
    Reading
    UK

    <b> 4<sup>th</sup> March 2016 </b>
<!--
<h2> stuff to read up on </h2>
<li> event attribution studies </li>
<li> DePreSys3/GloSea5 </li>
-->
<!--
<li> climate attribution </li>
<li> aerosols sources and impacts </li>
<li> hindcaste </li>
<li> Dr <NAME>; Dr <NAME>; Prof <NAME> </li>
-->
Dear Dr. <NAME>, Dr <NAME> and Prof <NAME>utton,

My name is <NAME>, and I would like to be considered for position the “Research Scientist” as part of LOTUS and DYVA projects exploring variability and changes in East Asian Climate. I have recently completed a PhD in climate change, where I utilised experimental and satellite data to developed and constrain a complex numerical vegetation model <citep>Kelley2014a</citep>. I used this model to explore the impacts of future climate change and climate extremes in Australia, including changes in fire <citep>Kelley2014b; Harrison</citep>, drought and hydrology <citep>Zeppel2014; Ukkola</citep>.

Many of these predicted impacts have important consequences for mitigation <citep>Kelley2014b</citep> and adaptation <citep>Ukkola; Harrison</citep> policies. However, these impact "forcasts" still remain largely qualitative due uncertainties in future climate variability and their impact on localised weather extremes (e.g. <cite>Friedlingstein2006; robson2010understanding; Harrison</cite>). Reducing near-term uncertainty (i.e seasonal, inter-annual and decadal) would be a huge step forward in qualitative prediction of extreme events, providing more accurate "early warning" seasonal and inter-annual forecasts which would aid preparation for potential impacts. Reducing this uncertainity requires attribution of extreme events between influences from different drivers including anthropogenic forcing, natural climate variability, or indeed the interplay between the two - an extremely complex and difficult task <citep>hartmann2013observations</citep>.

Several new climate model tools, such as GloSea5 (seasonal <cite>QJ:QJ2396</cite>) and DePreSys (inter-annual and decadal, <cite>smith2007objective</cite>), exploit low-frequency ocean oscillations, predictable for years to decades in advance <citep> boer2000study; boer2004long; boer2008multi; robson2010understanding</citep> to reduce uncertainty in inherent climate variability - the most important aspect of uncertainty for near-term predictions <citep>hawkins2009potential</citep>. Recent hindcasts experiments during specific ocean warming events using these tools has shown promising results for near-term predictability of terrestrial climate variability (e.g. <cite> Liu2012b; Robson2013; Knight2014</cite> for DePreSys), and would be ideal for exploring teleconnections between preceding ocean variability and extreme weather events. The East Asian Summer Monsoon (EASM) is a great example of where these tools could be used. Weakening of the EASM in recent decades - responsible for flooding in southern Asia and droughts in the North - has been attributed to a mix of drivers including global change from anthropogenic greenhouse gas emission <citep>hartmann2013observations</citep>, increases in atmospheric aerosols  associated with pollution and land use change <citep>Kaufman2002; Hsu2006; GRL:GRL22181; lau2006asian</citep> and changes in sea surface temperatures (SSTs), associated with the Pacific Decadal Variability and Atlantic Multidecadal Oscillation <citep>Chang2000; hartmann2013observations</citep>.
<NewPage>
Teasing apart the contribution of each driver, and the interactions between drivers, of East Asia extreme weather will be in itself fascinating, and I would relish the opportunity to apply the experience I gained from studying climate impacts in Australia to the challenge. Also, any improvement in our predictive understanding of the monsoonal system will directly benefit adaptation policies that could help the 1/3 of the worlds population that rely on monsoonal rain for their water security, a very worthwhile social endevour.
<!--
I believe that CSSP China programm is the ideal scheme for this – drawing on the strong meterological expertise from across the country, and regional experts at partnership institutes in China, will be vita

will call on some of ??regions experts??, and will help ensure research is directly applicable and relevant to the people the programme is designed to help. In short, the potential value of your research in terms of future environmental and political/social impacts cannot be overstated and it is for this reason that I would very much like to be a part of your team.
-->
Below, I have outlined how my experience in climate research makes me an ideal candidate for the role, with reference to the job description and selection criteria. Further details are also provided in my attached CV.


<!--<li> Knowledge of, or evidence of serious interest in, climate variability and climate change </li>
<li> <i> Knowledge of East Asian climate and monsoon dynamics </i> </li>
<li> <i> Knowledge of ocean-atmosphere interactions and teleconnections </i> </li>
<li> <b> Knowledge of climate event attribution </b> </li>
<li> Research in physical science; if not climate-related, evidence of the ability to acquire the relevant knowledge </li>
<li> Research in climate, ocean, atmosphere or Earth system modelling </li>
<li> Research in climate prediction </li>

<li> <citep> Kelley2014 </citep>: climate response detection
-->
<h2> Climate Science Knowledge Research Experience </h2>
I completed my PhD in Climate Change Ecology just over a year ago. My thesis (“Modelling Australian Fire Regimes”, <cite>kelley2014modelling</cite>), and subsequent postdoctoral research, focussed on climate dynamics in Australia, exploring climate controls on vegetation productivity and vegetation-fire interactions. It used satellite and ground-based observations of weather, climate and vegetation properties to develop and evaluate complex numerical vegetation and land surface models <citep>Prentice2011; Kelley2014a; Kelley; Hantson</citep>. I have driven some of these models with historical/reanalysis climate data to assess how teleconnections between ENSO and climate affect fire <citep>Kelley2008; Prentice2011</citep>. I drove the model I developed as part of my thesis – the LPX-Mv1 vegetation model <citep>Kelley2014a</citep> - with 21st century CMIP5 climate model outputs to predict climate response of ecosystem resilience and impact on the carbon cycle <citep>Kelley2014b</citep>, fire-dynamics <citep>Harrisonc</citep> and hydrology <citep>Ukkola</citep> in future climate. My thesis demonstrated the importance of incorporating climate-vegetation-fire interactions into present and future climate impact assessments, with my improved dry-climate vegetation simulations altering the magnitude, and in some cases the sign, of predicted future vegetation resilience and water availability. This included counterintuitive but important results that policymakers need to take into account, such as: CO2-related increases in vegetation may have added pressure on available water supply in already dry and drying regions, futher compounding predicted drought <citep>Ukkola</citep>; and that changes in the likelihood of fire events is dependent on the complex interactions between climate, vegetation compoistion/productivity and extreme whether events - not just extreme events by themselves <citep>Kelley2014b; Harrisonc</citep>.

In the dissertation for my MSc in Earth Systems Science, I incorporated a fire model into CCDAS -  a data assimilation system used to constrain physical based parameters for use in an Earth System Model framework -  using land-surface satellite observations <citep>Kelley2008; kaminski2013bethy</citep>. During my masters, I was also able to gain experience working with the GENIE Earth System Model, and I wrote a simple box-climate model. I am also currently coding a simple atmospheric transport model as part of a inter-model evaluation and benchmarking project (<a href = "http://douglask3.github.io/firemip.html" target = "_blank">http://douglask3.github.io/fireMIP</a>)

<h2> Academic Attainment </h2>
My examiners consistently stated my PhD thesis fell into the “top 10%” they've ever assessed. I completed my PhD just over one year ago, and I have obtained 7 peer-reviewed publications  – 3 of which are first authored – with a further 3 submitted and 3 near submission. One of these papers is in Nature Geoscience, and all others are in high-impact journals for their field.

<!--
<li> Very good computing skills. </li>
<li> Programming in Fortran/IDL/Python/<b>Unix shell </b> </li>
<li> <b> Design, execution and analysis of climate model simulations </b> </li>
<li> Substantial experience in computer programming and data analysis </li>
<li> Experience of numerical or climate modelling </li>
<li> Analysis of climate model and observational data </li>
-->
<NewPage>
<h2> Programming and Data Analysis <b>Skills</b> </h2>
The LPX model, that I have been the lead developer on for 6 years, combines FORTRAN (for the scientific component) and C++ (for the I/O component) and can be driven by large-scale climate datasets in either NetCDF or CSV format. I have also used and developed simple vegetation models, as well as a simple climate box-model as part of my masters, in Python, R and Matlab <citep>Kelley2013; Kelley; Whitley</citep>, and have performed statistical analysis and plotting in Python, R, Matlab and, during my masters, in IDL. I have developed some of my R-code into software packages, now available for anyone to download and use (e.g. <cite>Kelley2015; Whitley2016</cite> – both available from http://douglask3.github.io/tools), one of which has recently been adopted and applied to a new model inter-comparison project (fireMIP: <cite>Hantson2016; Hantson</cite>).

My work on developing and evaluating vegetation models has involved processing a range of climate/GIS data products, from raw and processed satellite data (see e.g. <cite>Kelley2008; Prentice2011; Kelley2013; Kelley2014a</cite> & <cite>Kelley2014b</cite>); vector ground observation (<cite>Kelley2013; Kelley2014a; kelley2014modelling</cite>); historical climate and climate reanalysis data <citep>Prentice2011; Kelley2014a; kelley2014modelling</citep>; and climate model data for past <citep>Ciais2011</citep> and future climates <citep>Kelley2014b; Harrison</citep>.


<h2> Communication Skills</h2>
During my PhD, I was given the opportunity to hone my presentation skills at many group and department seminars (see CV for a full list of presentations). As a result, I was awarded “best presentation” at the departments annual post-graduate conference, out of 78 other presenting students, and was awarded a travel grant to present results of simulations of future ecosystem resilience and carbon stocks at the AGU fall meeting <citep>kelley2013implications</citep>.

I have also attended several workshops, where I have given oral and poster presentations to audiences from a wide variety of disciplines (e.g. <cite>Kelley2008a; Kelleya; Kelley2013a</cite>).

In my own time, I designed my running clubs website (<a href = "http://www.eppingdac.com.au" target = "_blank">www.eppingdac.com.au</a>), published the clubs newsletter for two years, and was in charge of the clubs publicity for three, during which club membership increased by nearly 50%.
<!--
<li> Communicative and open to collaboration </li>
<li> Willingness to under-take visits to relevant institutions/events </li>
<li> Ability to maintain productive working relationships with collaborators at Reading and other institutions </li>
-->
<h2>Projects and Collaborations</h2>
I have been involved in several inter-university and interdisciplinary collaborations during my PhD. I led the design of a vegetation model benchmarking system  <citep>Kelley2013</citep> which  involved liaising with remote sensing experts, statisticians, botanists and other modellers from three different institutions. This system has been widely adopted throughout the vegetation modelling community and is used by several other modelling groups. I have also worked on three vegetation disturbance-trait databases, two of which I was a lead collaborator on <citep>Kelley2014a; kelley2014modelling; harrisonusing; Harrison</citep>.

Outside of my academic work, I was also involved in an international consultancy programme, preforming climate change risk analysis for the Royal Society for the Conservation of Nature (RSCN) in Jordan. Modelling work I conducted for this project focussed on the impacts of predicted short and<NewPage>long term changes in precipitation and rising temperature on ecosystem resilience and river flow, and was used by RSCN to determine areas of key ecological importance and relative stability suitable for nature reserves <citep>harrison2010impacts</citep>.
<!-- <li> Good communication skills both written and oral </li>
<li> Self-motivated, <b>conscientious and creative </b> </li> -->
<!--<li> Enjoy working with others </li>-->
<h2> Department and Research Group Involvement </h2>
During my PhD I acted as chair for my research groups' paper and research discussion meetings. I was also an informal mentor for several students in the group who were new to statistical programming, including a student who started their PhD as the result of a knowledge exchange initiative from the conclusion of the consultancy work conducted with the RSCN in Jordon, outlined above. This student has recently completed and passed their PhD. I also volunteered with the department's secondary school outreach programme, performing research demonstrations on school open days.

Whilst working as a research assistant at Bristol, I was involved in the teaching of the MSc Earth Systems Science masters programme, giving seminars on causes of, and controls on, wildfire, as well as running modelling workshops on programming for beginners.



Thank you for taking the time to review my application. I hope you agree that my climate research and numerical modelling experience, programming and climate data analysis skills, make me ideally suited to the Postdoctoral Researcher role.


Kind regards,

<NAME>



<h2> References </h2>
<References> ReadingMetChina/refList.bib <NewPage><NAME>. , <NAME>.</NewPage><NewPage>M</NewPage></References>
{'fraction_non_alphanumeric': 0.061773381919139336, 'fraction_numerical': 0.01950373069581815, 'mean_word_length': 5.310994305738064, 'pattern_counts': {'":': 0, '<': 396, '<?xml version=': 0, '>': 396, 'https://': 0, 'lorem ipsum': 0, 'www.': 4, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 2, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '336753', 'n_tokens_mistral': 7530, 'n_tokens_neox': 6803, 'n_words': 3878}