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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| 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 teacher head, 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".