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Record W3211174820 · doi:10.5281/zenodo.4270518

Guide sur le partage et le dépôt rapide des données sur la COVID-19 pour les chercheurs

2020· article· fr· W3211174820 on OpenAlexaff
Groupe de travail sur la Covid du Réseau Portage, Jane Fry, Chantal Ripp, Felicity Tayler, Minglu Wang, Kristi Thompson, Lucia Costanzo, Kathy Szigeti, Rebecca Dickson, Roger Reka, Nick Rochlin, Mark Leggott, Erin Clary, Beth Knazook, Melanie Parlette-Stewart

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2020
Typearticle
Languagefr
FieldHealth Professions
TopicHealthcare Systems and Practices
Canadian institutionsPortage CollegeYork UniversityUniversity of GuelphUniversity of WaterlooUniversity of WindsorCouncil of Prairie and Pacific University LibrariesUniversity of Ottawa
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)Medicine

Abstract

fetched live from OpenAlex

Le guide repose sur les recommandations du COVID-19 Working Group de la Research Data Alliance (RDA) : Recommendations and Guidelines for Data Sharing. Ces recommandations ont été conçues pour aider les chercheurs à se doter de bonnes pratiques pour le partage de données dans leur discipline et à maximiser l’impact de leur travail. Ce guide s'est inspiré en partie par les travaux précédents de Tayler et Ripp (2020) FAQ: Partage et dépôt de données en appui à une intervention rapide face à la COVID-19.

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)0.128
metaresearch head score (Gemma)0.253
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Open science
Consensus categoriesnone
DomainCandidate signal: Reproducibility · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.995
Threshold uncertainty score0.679

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1280.253
Meta-epidemiology (narrow)0.0010.003
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0080.005
Science and technology studies0.0040.004
Scholarly communication0.0110.008
Open science0.0050.010
Research integrity0.0060.009
Insufficient payload (model declined to judge)0.0410.039

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 head0.417
GPT teacher head0.431
Teacher spread0.013 · 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 designNot applicable
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

Citations0
Published2020
Admission routes1
Has abstractyes

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