Quantifying the Fecal Coliform Loads in Urban Watersheds by Hydrologic/Hydraulic Modeling: Case Study of the Beauport River Watershed in Quebec
Bibliographic record
Abstract
A three-step method for the identification of the main sources of fecal coliforms (FC) in urban waters and for the analysis of remedial actions is proposed. The method is based on (1) The statistical analysis of the relationship between rainfall and FC concentrations in urban rivers; (2) The simulation of hydrology and hydraulics; and (3) Scenario analysis. The proposed method was applied to the Beauport River watershed, in Canada, covering an area of 28.7 km2. FC loads and concentrations in the river, during and following rainfall events, were computed using the Storm Water Management Model (SWMM) hydrological/hydraulic simulation model combined with event mean concentrations. It was found that combined sewer overflows (CSOs) are the main FC sources, and that FC from stormwater runoff could still impair recreational activities in the Beauport River even if retention tanks were built to contain CSOs. Thus, intervention measures should be applied in order to reduce the concentration of FC in stormwater outfalls. The proposed method could be applied to water quality components other than FC, provided that they are present in stormwater runoff and/or CSOs, and that the time of concentration of the watershed is significantly lower than their persistence in urban waters.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".