Analysis of urban runoff control with infiltration facilities
Bibliographic record
Abstract
ABSTRACT This paper presents two methodologies for estimating the impact of infiltration facilities on reducing stormwater runoff volumes, and pollutant loads, from urban drainage systems. The methodologies have previously been developed using derived probability distribution theory, often referred to in the literature as analytical probabilistic modelling, and differ in that they employ different hydrologic models for the transformation of rainfall to runoff. Moreover, the original model derivations employed herein were developed for the analysis of stormwater detention facilities (dry ponds), and are adapted herein for the analysis of infiltration facilities. The summary of model expressions presented in this paper permits the reader to perform the necessary calculations to design and analyze the performance of stormwater infiltration facilities with the use of a calculator or computer-based spreadsheet application. Results are generated using the models, and various sensitivity analyses presented, illustrating the power of the models. The models are then used as a basis for comparison with guidelines on the design of such facilities in the province of Ontario, Canada. The results of this comparison suggest that the current guidelines may yield infiltration facilities insufficient in size to meet the performance levels intended to be satisfied by such facilities.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".