Bibliographic record
Abstract
The Etobicoke Exfiltration System (EES), a stormwater conveyance system best management practice, involves small modifications to the conventional storm sewer system design. In an effort to better quantify performance through modelling, this paper presents a methodology that was used to implement EES in the USEPA SWMM modelling framework and the result of applying the methodology on a case study. Key EES components, including the inlets, storage provided by sewer void space of the granular material in the sewer trench and the exfiltration from the trench into the soil, were modelled orifice, storage and pump components. The resulting methodology was applied to model a hypothetical EES retrofit in a 10.50 ha existing residential development serviced with conventional storm sewers and a pond. The results of continuous simulations show that the retrofit results in a significant reduction of runoff volume, water balance that approaches the predevelopment conditions, and drastic changes in flow-duration characteristics. The EES retrofit also results in significant peak flow reductions in response to design storm events, and improved system performance when subjected to precipitation loads modified by climate change.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".