Evaluation of Etobicoke Exfiltration System Applications in the City of Barrie
Bibliographic record
Abstract
These days engineers reduce the adverse effects of urbanizations using Low Impact Developments (LID) on their municipal design. Etobicoke Exfiltration System (EES) as a LID Best Management Practice (BMP) was demonstrated in 1993 and is being implemented at a hospital rehabilitation project in Toronto. To evaluate EES through modeling, a methodology was used to implement EES in SWMM 5.1.012, and the outcome was applied for a case study in Barrie. The primary components of EES include inlets, void space storage of granular material laid beneath the main sewer system. These components were modeled by orifices and a storage unit to simulate the exfiltration of water from the stone trench into the surrounding native soil. The model was applied in a case study in Barrie regarding hydrologic performance analysis. The results indicated a significant reduction of runoff volume and peak flow reduction for a single design storm. However, some challenges revealed by these results regarding the case study
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".