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Record W2958839295 · doi:10.1126/science.aaw2825

Certify reproducibility with confidential data

2019· article· en· W2958839295 on OpenAlexaff
Christophe Pérignon, Kamel Gadouche, Christophe Hurlin, Roxane Silberman, Eric Debonnel

Bibliographic record

VenueScience · 2019
Typearticle
Languageen
FieldDecision Sciences
TopicScientific Computing and Data Management
Canadian institutionsCascades (Canada)
FundersAgence Nationale de la Recherche
KeywordsConfidentialityMicrodata (statistics)CertificationComputer scienceAccreditationInternet privacyProcess (computing)Government (linguistics)Data scienceComputer securityMedicinePolitical scienceEnvironmental healthMedical education

Abstract

fetched live from OpenAlex

A trusted third party certifies that results reproduce

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)0.156
metaresearch head score (Gemma)0.525
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Reproducibility · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.844
Threshold uncertainty score0.823

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1560.525
Meta-epidemiology (narrow)0.0020.003
Meta-epidemiology (broad)0.0050.005
Bibliometrics0.0070.009
Science and technology studies0.0040.011
Scholarly communication0.0240.018
Open science0.0080.020
Research integrity0.0120.010
Insufficient payload (model declined to judge)0.0260.042

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 head0.309
GPT teacher head0.454
Teacher spread0.145 · 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.

Study designTheoretical or conceptual
DomainReproducibility
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

Citations40
Published2019
Admission routes1
Has abstractyes

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