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Record W2945891467 · doi:10.5281/zenodo.2578277

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

2019· report· en· W2945891467 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 D. 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, Yin-Min Huang, 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

VenueZenodo (CERN European Organization for Nuclear Research) · 2019
Typereport
Languageen
FieldComputer Science
TopicAdvanced Data Storage Technologies
Canadian institutionsAthabasca University
Fundersnot available
KeywordsCross disciplinaryOpen sourceOpen source softwareDisciplineEcosystemSoftwareComputer scienceData scienceSociologyEcologyOperating systemBiologySocial science

Abstract

fetched live from OpenAlex

We propose that the plasma physics community and funding agencies invest in an open source software ecosystem for plasma research and education.

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)0.004
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Open science, Insufficient payload (model declined to judge)
Consensus categoriesOpen science
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.671
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.000
Scholarly communication0.0070.003
Open science0.0100.024
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.117
GPT teacher head0.381
Teacher spread0.264 · 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
GenreMethods

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

Citations0
Published2019
Admission routes1
Has abstractyes

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