Forecasting receiving water response to alternative control levels for combined sewer overflows discharging to Toronto’s Inner Harbour
Bibliographic record
Abstract
This article evaluates the role that different levels of control for combined sewer overflows have in addressing the recreational water quality objectives of the Toronto Inner Harbour of the Toronto and Region Remedial Action Plan. Three models are used to establish the predictive methodology: the Infoworks model for the combined sewer service area, the Hydrologic Simulation-F model for the remainder of the watershed, and the MIKE 3 computer code to evaluate Lake Ontario response to control. Each model was calibrated with E. coli densities observed respectively in sewer discharges, instream, and in the Inner Harbour. Two indices are used to evaluate the response of water quality in the Inner Harbour – fraction of the surface area achieving Blue Flag status, and portion of the swimming season (June to August) above recreation objectives. Analyses of control options led to the recommendation that virtual elimination of combined sewer overflows (one overflow per season control strategy) should be pursued, rather than the lower level of control of 90% volumetric control, which is the minimum provincial environmental requirement. Implementation of priority projects for improving water quality along the Toronto waterfront, including the Don River and Central Waterfront project, are integral to delisting Toronto as a Great Lakes Area of Concern.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".