Développement d'un modèle conceptuel pour l'évaluation de la demande en eau urbaine future : application à une ville Québécoise
Bibliographic record
Abstract
RÉSUMÉ: «RÉSUMÉ:La demande en eau future d’une ville moyenne québécoise sous l’impact du changement climatique, de la démographie et des mesures d’économie d’eau a été évaluée pour la période de 2025 à 2055 par le développement d’un modèle, basé d’une part sur la régression linéaire simple, et d’autre part sur une analyse par scénarios. L’approche par essais-erreur a été utilisée, i.e., le modèle s’est construit suivant les résultats obtenus à chaque étape. Dans le cadre de ce projet, nous nous somme limités à l’évaluation de l’impact de la température maximale de l’air ( ABSTRACT: «ABSTRACT: The future water demand of a medium city in Quebec under the impact of climate change, demographics, and water-saving measures has been assessed for the period from 2025 to 2055 by developing a model based on both simple linear regression and scenario analysis. The trial-and-error approach was used, i.e., the model was built based on the results obtained at each stage. Within the scope of this project, we focused on assessing the impact of maximum air temperature (
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".