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Record W4250163777 · doi:10.2172/1512719

Building an open source software ecosystem for cross-disciplinary plasma research and education

2019· report· en· W4250163777 on OpenAlexaff
Nicholas A. Murphy, Dominik Stańczak, Andrew J. Leonard, T. N. Parashar, Pawel Kozłowski, B. L. Alterman, D. A. Roberts, Steven Christe, Martin Connors, Monica Bobra, James Mason, Will Barnes, Ryan McGranaghan, Asti Bhatt, P. J. Erickson, Frank Lind, Ryan Volz, John Swoboda, Nick Hatzigeorgiu, Andrew Inglis, Felipe Nathan de Oliveira Lopes, J. Ireland, John Coxon, Sophie A. Murray, Japheth Yates, Mark C. M. Cheung, J. Klenzing, David Stansby, Han He, Chuanfei Dong, H. D. Winter, Juan-Camilo Buitrago-Casas, Manjit Kaur, S. P. Smith, B. Dudson, Daniel B. Seaton, Luca Comisso, Alexa Halford, Daniel Barnak, R. S. Weigel, Antoine Tavant, J. D. Vandegriff, M. de Val-Borro, Antonia Savcheva

Bibliographic record

Venuenot available
Typereport
Languageen
FieldDecision Sciences
TopicScientific Computing and Data Management
Canadian institutionsAthabasca University
FundersLos Alamos National LaboratoryNational Nuclear Security AdministrationU.S. Department of Energy
KeywordsSoftwareComputer scienceSoftware analyticsSoftware engineeringSoftware developmentDisciplineCross disciplinaryData scienceSystems engineeringSoftware constructionEngineeringSociologyOperating systemSocial science

Abstract

fetched live from OpenAlex

Software is crucial to all areas of modern plasma science research. Laboratory plasma physicists use software to interpret plasma diagnostics, analyze experimental results, and glean insights using advanced techniques from data science. Space scientists use software to reduce and understand in situ observations. Numericists use software to simulate the behavior of laboratory, heliospheric, and astrophysical plasmas, and then analyze or visualize the results. Theorists use symbolic manipulation software to perform or check derivations. Cross-disciplinary research and cross-comparisons between experiments, observations, simulations, and theories all require software. Despite our heavy reliance on software, funding agencies have traditionally had few avenues available to support development of general purpose research software infrastructure.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.057
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Scholarly communication, Open science
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.659
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0570.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0120.001
Open science0.0050.009
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.516
GPT teacher head0.588
Teacher spread0.072 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2019
Admission routes1
Has abstractyes

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