Two Sides, One Flow: Rethinking Urban Stormwater Infrastructure Performance with Stakeholder Perceptions
Bibliographic record
Abstract
Assessing stormwater infrastructure performance is essential for informed asset management. While many studies have designed frameworks for evaluating infrastructure performance as a whole, they often overlook the unique aspects of different systems, leading to concerns about the comprehensiveness of these frameworks. Additionally, the perspectives of both agencies (operating municipalities) and customers (the public) on stormwater performance are underexplored. This study introduces a detailed performance evaluation framework that integrates levels of service (LOS) with agency and customer viewpoints. A survey was conducted among Ontario, Canada-based agencies and customers to gauge their perceptions of the importance of various LOS dimensions. By using fuzzy composite programming, the framework evaluates stormwater performance based on survey responses and operational data from a large-scale municipality in Ontario, Canada. The findings revealed that agencies and customers have a significant alignment in their views on LOS, with minor differences in quantity, responsiveness, and environmental acceptability. Agencies valued safety, quality, and environmental acceptability the most, while customers prioritized quality, quantity, and cost. The study’s case analysis showed that customer perception of stormwater infrastructure performance is slightly lower than that of the agency. Sensitivity analysis further highlighted seven key performance indicators that asset managers should prioritize to maintain satisfactory infrastructure performance.
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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 | 0.021 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".