MétaCan
Menu
Back to cohort
Record W6974138483 · doi:10.58079/ng2m

Participez à une enquête internationale sur le vélo avant et pendant la crise sanitaire (Covid-19) !

2021· article· fr· W6974138483 on OpenAlexaboutno aff

Bibliographic record

VenueIndustrias Culturais (Universidade de Coimbra) · 2021
Typearticle
Languagefr
FieldMathematics
TopicCOVID-19 epidemiological studies
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)Government (linguistics)Relation (database)Face (sociological concept)Perspective (graphical)

Abstract

fetched live from OpenAlex

Coronapistes, coup de pouce vélo, hausse nette du nombre de cyclistes… le vélo en ville rencontre un grand succès depuis le début de la crise de la Covid-19. Une équipe de chercheurs et de chercheuses lance une enquête en France, en Suisse, au Canada et en Colombie pour connaître les expériences et pratiques du vélo, les motivations de celles et ceux qui pédalent, ainsi que leurs évolutions liées à la crise sanitaire. La crise de la Covid-19 a encouragé les pouvoirs publics à repenser l’util...

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)0.004
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.099
Threshold uncertainty score0.196

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.002
Scholarly communication0.0060.003
Open science0.0010.008
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0320.003

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 head0.226
GPT teacher head0.373
Teacher spread0.147 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueIndustrias Culturais (Universidade de Coimbra)Same topicCOVID-19 epidemiological studiesFrench-language works237,207