Stormwater Quality Modeling Improvements Needed for SWMM
Bibliographic record
Abstract
The U.S. Enviromnental Protection Agency's (USEPA) Stonnwater Management Model, or S WMM, is a large, relatively complex software package capable of simulating the transformation of precipitation to urban nmoff and the transport of the runoff from the grmmd surface through pipe/channel networks and storage/treatment facilities and finally to receiving waters. The model can be used to simulate a single event or a long continuous period. The original model was developed by Metcalf and Eddy, Inc. in association with the University of Florida and Water Resources Engineers, Inc. in 1971. Over the last three decades there have been many significant improvements and enhancements to the model's capabilities. However, the model's algorithms used to simulate the accumulation and transport of storm water pollutants have rarely been addressed or significantly improved. With the recent development of the USEP A's NPDES storm water program and the ongoing development of the TMDL program and the continuing interest and concern associated with storm water pollution throughout the developed world, the need to significantly improve the stormwater quality modeling capabilities ofSWMM is greater now than ever before.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".