Development and Calibration of a Dual Drainage Model for the Cooksville Creek Watershed, Canada
Bibliographic record
Abstract
Under storm conditions urban stormwater drainage systems may undergo various flow regimes including backwater, surcharging, reverse flow and surface ponding.Evaluation of the performance of a stormwater management system under these different conditions requires detailed hydraulic grade line analysis of both the minor and major drainage systems.Computational Hydraulics International, the Credit Valley Conservation Authority and the City of Mississauga have worked in collaboration to develop a high resolution hydrologic-hydraulic dual drainage model to address these needs for the highly urbanized and flood vulnerable Cooksville Creek watershed.This paper presents the model development, parameterization, calibration and validation.The completed model consists of >4 000 subcatchments covering the 33 km 2 drainage area and >8 000 conduits representing >500 km of drainage network including storm sewer pipes, major system flow paths, ditches and the creek itself.A comparison of observed and computed maximum and total flow volumes at the creek's flow gauge for 12 calibration events yielded Nash-Sutcliffe model efficiency coefficient values of 0.86 and 0.82 respectively.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".