A closer look at Toronto's water quality control design criteria for bioretention cells
Bibliographic record
Abstract
Bioretention cells in the Toronto region are usually sized to accommodate runoff from the 90th percentile storm, which has a depth of about 25 mm. This research examines the water quality control performance and cost of bioretention cells sized to satisfy alternative design criteria ranging from 5 to 50 mm. The long-term average runoff-capture efficiencies provided by representative bioretention cells are determined, and their capital as well as operation and maintenance costs are estimated. Results indicate that the current design criterion of 25 mm is probably too high and not cost-efficient. In fact, above some threshold levels, little improvement in runoff-capture and pollutant removal performances may be achieved if the design criterion is increased further, but cost would still linearly increase. Presented here is a methodology that can be used to properly consider both the performance and cost of bioretention cells for establishing a more cost-efficient design criterion. It is shown that a more cost-efficient design criterion for Toronto could be lower than the current one, and significant cost savings can be realized if a lower design criterion is implemented. Some inappropriate ways of quantifying the effective volume of storage provided by bioretention cells are also identified in this paper.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".