Development of a Multi-Category Decision-Making Framework to Identify Stormwater Reuse Design Factors in Mixed-Use Communities
Bibliographic record
Abstract
Municipalities are experiencing a growing water management challenge as a result of population growth in water-dependent communities. Population growth and stricter regulations on enhanced stormwater management have motivated some municipalities to reuse stormwater. Stormwater reuse can reduce potable water demand and encourage water conservation, alleviating the burden on existing drinking water and stormwater infrastructure and potentially delay their future expansion. Although stormwater reuse is a relatively new and unregulated practice within Canada, the rising cost of potable water, in addition to limits placed on stormwater discharges, provide opportunities for its adoption. The aim of the paper is to present a novel decision-making framework to identify design factors and approaches which may aid municipalities in the implementation of stormwater reuse. The decision-making framework includes the following factors: quantity of reused water; treatment of reused water; water reuse pricing; site constraints in reuse implementation; policy considerations in reuse implementation. Potential subcategories and methods of structuring the framework are also discussed.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".