Risk-Based Performance Assessment of Stormwater Drainage Networks under Climate Change: A Case Study in the City of Kingston, ON
Bibliographic record
Abstract
The ability of urban drainage systems to operate satisfactorily under a wide range of possible future hydrologic conditions is an important system characteristic. As weather patterns shift, it is important to understand how local infrastructure may be affected as extreme rainfall events have the potential to cause direct and indirect damages to communities. Continuous simulation in SWMM 5.1 coupled with synthetic precipitation files generated using GCM outputs were used to assess the risk-based performance in terms of reliability, resiliency, and vulnerability of an urban drainage system in the city of Kingston, Ontario, Canada. The study drainage network investigated in this paper never experienced a flooding or surcharging event (i.e., 100% system reliability), however, an observed positive trend in the ratio of conduit depth to full depth over time indicates the potential for unsatisfactory system performance in the future as a result of changing hydrologic conditions due to climate change.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".