Methodology for Selecting Best Management Practices Integrating Multiple Stakeholders and Criteria. Part 2: Case Study
Bibliographic record
Abstract
The selection of stormwater Best Management Practices (BMPs) for mitigating the effects of urbanization on the hydrological cycle could be a complex process due to conflicting stakeholder views, and varying levels of performance of BMPs across a range of criteria (runoff reduction, erosion control, etc.). Part 1 of this article proposed a methodology based on the application of multi-criteria decision aid (MCDA) methods, which was tested here on a residential stormwater network in the Montreal area. The case study considered green roofs, rain gardens, rain barrels and pervious pavement over a range of economic, social, and water quality and quantity criteria by applying 4 MCDA methods under three different stakeholder views. The results indicated Elimination et Choix Traduisant la Réalité (ELECTRE) III to be the most appropriate method for the methodology, presenting flexibility concerning threshold values, criteria weights, and showing shared top choices across stakeholders (rain gardens, and rain gardens in combination with pervious pavement). The methodology shows potential for more formal applications and research opportunities. Future work may lie in the inclusion of multiple objective optimization, better stakeholder engagement, estimation of economic benefits, water quality modeling, long-term hydrological simulations, and estimating real BMP pollutant removal rates.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".