Methodology Selection of Best Management Practices and Economic Analysis: A Case study in Quebec, Canada Méthodologie pour choisir les pratiques de gestion optimale et analyse économique: Une étude de cas au Québec, Canada
Bibliographic record
Abstract
The paper describes a methodology to be used when planning to implement the Best Management Practices (BMP) in an urban sector. This methodology has been tested in a case study of a sector of residential development in a municipality of Quebec in Canada. The performance analysis, on one hand, makes possible the hydrologic gain of each BMP to be determined in comparison with traditional practices. The economic analysis, on the other hand, shows that the establishment of some BMP in Quebec, on the basis of a few identified objectives of sustainable stormwater management, is both feasible and beneficial. Nevertheless, an in-depth technical analysis is still necessary to identify and design the BMP that will be selected to improve the urban drainage in Quebec as well as a detailed economic analysis to determine the real costs involved in the investment of new techniques.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".