Les caroténoïdes sériques comme biomarqueurs : une stratégie pour améliorer la validité de l’évaluation alimentaire
Bibliographic record
Abstract
La marge d’erreur dans l’évaluation de la prise alimentaire au moyen des outils traditionnels comme le rappel de 24 h, le questionnaire de fréquence et le journal alimentaire est grande et peut conduire à l’interprétation erronée de résultats de recherche. La recherche sur des biomarqueurs associés à la consommation de fruits et de légumes a le potentiel d’améliorer sensiblement la validité de l’évaluation de la prise alimentaire ainsi que la mesure des associations entre la qualité nutritionnelle et la santé. Les caroténoïdes, des pigments issus presque exclusivement du monde végétal, présentent un intérêt grandissant dans ce domaine. Les caractéristiques des caroténoïdes ainsi que les avantages et les défis que pose leur utilisation dans l’évaluation nutritionnelle seront explorés dans la présente revue.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.109 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".