An innovative method for selecting efficient best management practices
Bibliographic record
Abstract
The current state of the stormwater management in the province of Quebec in Canada indicates that the issue of uncontrolled rainwater rejection into water receivers is gaining interest from water infrastructure managers. Structural Best Management practices (BMPs) have drawn significant attention given their importance in improving water quality, in effectively addressing water quantity-related issues, and in establishing norms for regulatory compliance. The aim of this paper is to allow current BMPs selection – which at present , due to the lack of appropriate documentation, largely rely on subjective considerations – to be processed through an efficient and innovative methodology as to guarantee the selection of optimal BMPs for a given city area. Such a methodology would enable technical professionals to provide proven and valuable guidance to decision-makers and citizens for the adoption of more sustainable stormwater managing avenues. The proposed method – based on the Analytical Hierarchy Process (AHP) – was applied to a case study in Quebec, and its promising results are here presented.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".