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Record W3005445155 · doi:10.1093/pch/pxz101

L’alimentation en milieu scolaire : appuyer l’offre d’aliments et de boissons sains

2020· article· fr· W3005445155 on OpenAlexaffabout
Jeffrey Critch

Bibliographic record

VenuePaediatrics & Child Health · 2020
Typearticle
Languagefr
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsCanadian Paediatric Society
Fundersnot available
KeywordsPolitical scienceHumanitiesArt

Abstract

fetched live from OpenAlex

Résumé L’adoption de politiques et de directives alimentaires dans les écoles canadiennes permet d’accroître l’offre et la consommation d’aliments riches en nutriments tout en réduisant l’accès à des aliments et des boissons riches en sucres, en sodium et en gras saturés. Ces politiques favorisent des changements positifs pour la santé des enfants et des adolescents, tels qu’un meilleur indice de masse corporelle. Cependant, elles ont des effets mitigés sur la performance scolaire. Le présent document de principes présente les principaux éléments des politiques alimentaires en milieu scolaire, notamment les normes nutritionnelles. Ces politiques doivent respecter les recommandations du Guide alimentaire canadien et promouvoir la consommation d’aliments et de boissons riches en nutriments, dont la teneur en gras saturé, en sucre et en sodium est plus faible.

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)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0030.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.019
GPT teacher head0.317
Teacher spread0.297 · 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 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

Citations1
Published2020
Admission routes2
Has abstractyes

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