Innovative Stormwater Design for Runoff Volume and Peak Flow Reduction at the Source
Bibliographic record
Abstract
The emphasis to maintain pre-development hydrology and attain runoff volume target control, have made the use of Low Impact Development (LID) stormwater management practices essential. Innovative LID practices like Etobicoke Exfiltration System (EES) and Damming Baffles (herein referred as Inline Flow Restrictors (IFRs)) are used to promote exfiltration and detention of runoff, that addresses the stormwater goals of urbanized area with limited space for LID. An existing, calibrated Dual Drainage (DD) model is modified using USEPA SWMM (v5.2) to explicitly model the minor and major system flows, for study area in the Town of Richmond Hill, Ontario. The model investigates the hydrologic performance of dual drainage system with EES, and IFRs as compared to a dual drainage system with wet pond, in terms of runoff volume and peak flow reduction. Model results indicate significant runoff volume reduction and manageable maximum peak flow with respective addition of the EES and IFRs.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".