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Record W2614861664 · doi:10.1145/3096730.3096731

Some steps to improve software information

2017· article· en· W2614861664 on OpenAlexaff
Albert Heinle, Wolfram Koepf, Wolfram Sperber

Bibliographic record

VenueACM communications in computer algebra · 2017
Typearticle
Languageen
FieldDecision Sciences
TopicScientific Computing and Data Management
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsComputer scienceMathematical softwareSoftwareMetadataKey (lock)CitationSoftware engineeringData scienceWorld Wide WebProgramming languageComputer security

Abstract

fetched live from OpenAlex

Mathematical software plays an increasing role in mathematics and key technologies. But to find and get information about software is expensive. Missing metadata standards, e.g., the citation of software, are one of the reasons. In the following some recent developmentsare described how the community can contribute to a better information system for mathematical software.

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)0.031
metaresearch head score (Gemma)0.061
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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.031
Threshold uncertainty score0.166

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0310.061
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0110.007
Science and technology studies0.0040.005
Scholarly communication0.0110.040
Open science0.0060.008
Research integrity0.0070.010
Insufficient payload (model declined to judge)0.0250.013

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 head0.138
GPT teacher head0.406
Teacher spread0.268 · 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
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

Citations3
Published2017
Admission routes1
Has abstractyes

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