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Record W4300096145 · doi:10.25080/majora-14bd3278-011

Proceedings of the 13th Python in Science Conference

2014· paratext· en· W4300096145 on OpenAlexaff
Gaël Varoquaux, Stefan van Der Walt, Jarrod Millman

Bibliographic record

VenueProceedings of the Python in Science Conferences · 2014
Typeparatext
Languageen
FieldComputer Science
TopicComputational Physics and Python Applications
Canadian institutionsUniversity of Waterloo
FundersPennsylvania State UniversityUniversity of EdinburghUniversity of Pennsylvania
KeywordsPython (programming language)Computer scienceProgramming language

Abstract

fetched live from OpenAlex

The physics community is working to improve the undergraduate curriculum to include computer skills that graduates will need in the workforce.At Penn State Erie, The Behrend College, we have added computational tools to our Junior/Senior physics laboratory, PHYS421w Research Methods.The course emphasizes Python software tools (SciPy) for data analysis rather than traditional programming.The course uses real experiments to motivate the mastery of these tools.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.237
Threshold uncertainty score0.793

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.014
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0070.008
Open science0.0040.007
Research integrity0.0020.007
Insufficient payload (model declined to judge)0.2370.137

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 head0.027
GPT teacher head0.288
Teacher spread0.261 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations58
Published2014
Admission routes1
Has abstractyes

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