MétaCan
Menu
Back to cohort
Record W6930917041 · doi:10.5281/zenodo.4046659

Puis-je partager mes données ?

2020· article· fr· W6930917041 on OpenAlexaffabout

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2020
Typearticle
Languagefr
FieldNeuroscience
TopicNuclear Receptors and Signaling
Canadian institutionsPortage CollegeYork UniversityUniversity of GuelphUniversity of WaterlooUniversity of WindsorCouncil of Prairie and Pacific University LibrariesUniversity of Ottawa
Fundersnot available
KeywordsContext (archaeology)CaesalpinioideaeConfusion

Abstract

fetched live from OpenAlex

Cet arbre décisionnel a été conçu pour aider les chercheurs canadiens à repérer les situations où les données de participants doivent être dépersonnalisées ou rendues anonymes avant d’être consignées dans un dépôt. Il repose considérablement sur l’Énoncé politique des trois conseils : Éthique de la recherche avec des êtres humains — EPTC 2 (2018). Cet énoncé canadien porte sur le consentement et l’utilisation secondaire des données dans la recherche.

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)0.114
metaresearch head score (Gemma)0.285
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesOpen science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.997
Threshold uncertainty score0.602

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1140.285
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0040.003
Science and technology studies0.0060.007
Scholarly communication0.0160.024
Open science0.0030.010
Research integrity0.0060.008
Insufficient payload (model declined to judge)0.0460.022

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 head0.096
GPT teacher head0.256
Teacher spread0.160 · 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
Domainnot available
GenreEmpirical

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 routes2
Has abstractyes

Explore more

Same venueZenodo (CERN European Organization for Nuclear Research)Same topicNuclear Receptors and SignalingFrench-language works237,207