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Record W2620238560 · doi:10.1126/scitranslmed.aai9055

A trust approach for sharing research reagents

2017· review· en· W2620238560 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueScience Translational Medicine · 2017
Typereview
Languageen
FieldComputer Science
TopicResearch Data Management Practices
Canadian institutionsOccupational Cancer Research CentreMaple Leaf Medical ClinicStructural Genomics ConsortiumUniversity of Toronto
FundersEshelman Institute for Innovation, University of North Carolina at Chapel HillNovartis PharmaAbbVieEuropean Federation of Pharmaceutical Industries and AssociationsUniversity of TorontoOntario Ministry of Research, Innovation and ScienceWellcome TrustEuropean CommissionBoehringer IngelheimBill and Melinda Gates FoundationPfizerFundação de Amparo à Pesquisa do Estado de São PauloGenome Canada
KeywordsFeature (linguistics)Core (optical fiber)Property (philosophy)BusinessPublic goodPublic trustInternet privacyComputer sciencePublic relationsLaw and economicsPolitical scienceEconomicsMicroeconomicsTelecommunications

Abstract

fetched live from OpenAlex

The core feature of trusts-holding property for the benefit of others-is well suited to constructing a research community that treats reagents as public goods.

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.

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)0.028
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication, Open science
Consensus categoriesScholarly communication
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.984
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0280.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0020.002
Scholarly communication0.0030.020
Open science0.0180.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.790
GPT teacher head0.613
Teacher spread0.177 · 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