City of Toronto's Wet Weather Flow Management Master Plan
Bibliographic record
Abstract
Urban development within the City of Toronto and surrounding regions has adversely impacted the aquatic environment. Changes to the hydrologic cycle and land use practices have increased flows and contaminant contributions to area surface waters, during wet weather, from storm sewer and combined sewer overflow discharges. The City of Toronto has developed a Wet Weather Flow Management Master Plan to address the impacts of wet weather flows. The study area extended across the City of Toronto, encompassing six major watersheds and the waterfront. A new philosophy in wet weather flow management was adopted which recognized rainwater as a resource; wet weather flows were to be managed on a watershed basis; and a hierarchical approach to wet weather flow management was to be used, starting with at source, followed by conveyance and finally end-of-pipe control measures. A series of 13 objectives were identified, grouped into four major categories: water quality, water quantity, natural areas and wildlife, and sewer system. An innovative approach integrating hydrologic, hydraulic and water quality predictions from land based, watershed and lake models, respectively, was used to assess the effectiveness of various strategies. The receiving water response indicated that source controls and conveyance controls were insufficient to achieve the receiving water objectives of the Plan. The Plan's objectives can be met only through the implementation of a comprehensive set of measures consisting of: source controls, conveyance controls, end-of-pipe controls, basement flooding protection works, stream restoration works, shoreline management, enhanced municipal operations and an enhanced public education and community outreach program.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.004 |
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".