Proposition d'un outil d'évaluation de la performance globale de pratiques de gestion optimales basé sur la méthode multicritère d'aide à la décision AHP
Bibliographic record
Abstract
RÉSUMÉ: L'implantation de pratiques de gestion optimales (PGOs) et leurs études ont permis de mieux comprendre leur fonctionnement et de ce fait d'améliorer les techniques d'évaluation de leur performance. Toutefois, la littérature n'offre pas de méthode d'évaluation de la performance globale d'une PGO, mais plutôt des méthodes permettant d'évaluer une PGO sous plusieurs critères. C'est pourquoi ce mémoire se penche sur la mise en oeuvre d'un outil de calcul permettant d'évaluer la performance globale d'une ou de plusieurs PGOs. Après considération, il a été décidé que l'outil de calcul serait basé sur la méthode « Analytical Hierarchy Process » (AHP), une méthode d'aide à la décision multicritère. Cette méthode permet d'intégrer facilement des poids différents pour chacun des critères d'évaluation de la performance globale. Toutefois, c'est surtout son exécution simple qui a été le point décisif dans le choix de la méthode. De plus, l'utilisation de la méthode AHP permet d'intégrer des sous-critères à l'évaluation de la performance globale. Finalement, l'outil de calcul peut évaluer plusieurs PGOs en même temps. ABSTRACT: The implementation of best management practices (BMPs) and their studies have led to a better understanding of how they work and, as a result, to improved techniques for evaluating their performance. However, the literature does not provide a method for evaluating the overall performance of a BMP, but rather methods for evaluating a BMP under several criteria. Therefore, this thesis focuses on the implementation of a tool to evaluate the overall performance of one or more BMPs. After consideration, it was decided that the calculation tool would be based on the Analytical Hierarchy Process (AHP) method, a multi-criteria decision aid (MCDA). This method makes it easy to incorporate different weights for each of the criteria for evaluating overall performance. However, it is mainly its simple execution that was the decisive point in the choice of the method. In addition, the use of the AHP method allows for the integration of sub-criteria into the overall performance evaluation. Finally, the calculation tool can evaluate several BMPs at the same time. In order to demonstrate the applicability of the tool to assess the overall performance of BMPs, a case study is presented. The case study covers a vegetated retention basin and bioretention basin project located on Papineau Avenue in Montreal, Quebec, Canada. In the case study, the performance evaluation criteria were their capacity to retain rainfall of less than 19 mm, their capacity to control runoff water so that contaminant concentrations at the outlet of the basins respect the permitted thresholds and finally their capacity to generate savings compared to a standard development.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".