Urban Stormwater Quality Control Analysis with Detention Ponds
Bibliographic record
Abstract
This paper presents methodologies for the development of stormwater quality control measures based on the derived probability distribution approach. These stormwater control measures, such as the fraction of pollutant removed from storage facilities, are closed-form analytical models and can be effectively used to evaluate pollutant loads to receiving waters. In this study, a simple form of rainfall-runoff transformation with lumped parameters is first extended to take into account the spatial variations in model parameters. Second, the infiltration process is further incorporated to the rainfall-runoff transformation. This study demonstrates that analytical models can be developed with various levels of complexity based on different hydrologic considerations. The performance of the analytical models is evaluated in a case study, and the results indicate that, with an appropriately formulated rainfall-runoff transformation, analytical stormwater runoff models are capable of providing comparable results to continuous simulation models in the evaluation of the long-term performance of storage facilities.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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; both teacher heads agree on what is shown here.
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".