Investigation of the Sediment Removal Frequency for Wet-Detention Stormwater Management Ponds
Bibliographic record
Abstract
The purpose of this study was to develop methodologies for determining the appropriate sediment removal frequency of wet-detention stormwater management facilities. Using data from a wet pond in the Town of Richmond Hill, a sediment accumulation model was developed using the US EPA’s Stormwater Management Model (Version 5). Two different methodologies were then developed and applied to the facility. The first methodology is a real-time tool that provides the required time for sediment removal for a single cleanout cycle. The second methodology is analysis tool that relates cleanout frequency, annual cost and violations over a 50-year planning period. The results showed that the appropriate sediment removal frequency for the pond was approximately 16 to 17 years, and the annual cost of sediment removal ranged from $2,150 to $2,372, depending on the methodology used. It is recommended that these methodologies be considered for the planning and operation phases of stormwater ponds.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".