Réduire le gaspillage et les pertes alimentaires : Quels sont les facteurs de succès ?
Bibliographic record
Abstract
« L’humanité gaspille plus d’un milliard de repas par jour. » C’est ce qu’affirmait le Programme des Nations Unies pour l’environnement (PNUE) le 27 mars 2024 à l’occasion de la publication du plus récent rapport sur l'indice de gaspillage alimentaire. Chaque habitant de la planète gaspillerait en moyenne 79 kg de nourriture, ce qui représente environ 1 milliard de tonnes de déchets alimentaires. Au Québec, la quantité totale de résidus alimentaires produite annuellement s’élève à plus de 3 millions de tonnes selon RECYC-QUÉBEC, ce qui équivaut à 1 kg par habitant par jour. Comment y remédier ? Une étude CIRANO (Cloutier et al., 2024) propose des pistes de réflexion quant aux facteurs clés de succès pour la mise en œuvre d’initiatives visant la réduction des pertes et gaspillages alimentaires.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".