MétaCan
Menu
Back to cohort
Record W4379528922 · doi:10.1109/mcse.2023.3277663

Table of Contents

2022· article· en· W4379528922 on OpenAlexaff
Jeffrey C. Carver, Nasir U. Eisty, Hai Ah Nam, Irina Tezaur, Ian A. Cosden, Kenton McHenry, Daniel S. Katz, Michael A. Heroux, Nils Wedi, Peter Bauer, Irina Sandu, Jörn Hoffmann, Sophia Sheridan, Rafael Cereceda, Tiago Quintino, Daniel Thiemert, T. Geenen, Victorino Sanz, Alfonso Urquía, Yong Wan, Holly A. Holman, Charles Hansen, Sonia López Alarcón, Anne C. Elster, Anshu Dubey, Dali Wang, Peter Schwartz, Fengming Yuan, Peter Thornton, Weijian Zheng, Todd Gamblin

Bibliographic record

VenueComputing in Science & Engineering · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicDiverse Scientific and Economic Studies
Canadian institutionsImpact
Fundersnot available
KeywordsComputer scienceTable (database)Theoretical computer scienceComputer graphics (images)Data mining

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.001
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.115
Threshold uncertainty score0.164

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.004
Science and technology studies0.0020.000
Scholarly communication0.0050.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.8850.850

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 head0.037
GPT teacher head0.200
Teacher spread0.164 · 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; the direct Gemma label and the distilled Codex classifier 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

Citations0
Published2022
Admission routes1
Has abstractno

Explore more

Same venueComputing in Science & EngineeringSame topicDiverse Scientific and Economic StudiesFrench-language works237,207