Développement d'un outil paramétré pour l'optimisation environnementale de la séquence de traitement de matières résiduelles avec prise en compte de la qualité des flux de matière
Bibliographic record
Abstract
RÉSUMÉ: Dans le contexte d'une transition vers une économie circulaire, un besoin existe pour des outils d'aide à la décision permettant d'évaluer de façon prospectice les performances environnementales de systèmes de gestion des matières résiduelles. Or, les impacts directs et indirects ainsi que les bénéfices environnementaux de procédés, et particulièrement en gestion des matières résiduelles où la composition des flux de matières est très hétérogène et variable, sont sensibles à leurs paramàtres d'opération, aux compositions et propriétés de la matière entrante et à la qualité des produits de valorisation substituts dans l'économie. Les outils de quantification des impacts environnementaux actuellement disponibles ne permettent pas de prendre tous ces facteurs en considération, et ce, dans un contexte québécois. ABSTRACT: In the context of a transition to a circular economy, there is a need for decision support tools that allow for prospective evaluation of waste management systems' environmental performances. The direct and indirect impacts as well as the environmental benefits of processes, particularly in waste management where the composition of material flows is heterogeneous and variable, are sensitive to their operating conditions, to the composition and properties of the incoming materials and to the quality of the recovery products substituted in the economy. The tools currently available for quantifying environmental impacts do not take all of these factors into consideration, nor do they do so in a Quebec context.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".