Bibliographic record
Abstract
In September 2010, the U.S. Environmental Protection Agency (USEPA) released a new version (5.0.021) of Storm Water Management Model Version 5 (SWMM5) that offers low impact development (LID) modeling capability for the first time.The same LID modeling capability was soon enabled in the PCSWMM software (Version 2010, from Computational Hydraulic Int.).Five types of LIDs can be modeled in SWMM5 and PCSWMM: bioretention cells (rain gardens), infiltration trenches, porous pavement, cisterns (rain barrels) and vegetative swales.Using these new software releases and real world examples, this chapter presents some LID modeling features of SWMM5 and PCSWMM, input data requirements, modeling procedures, and output results for quantifying the LID impacts on sewer overflows.The modeling results presented here can help quantify the LID impacts on sewer overflows and allow sustainable developers to answer questions such as How much rainfall can be captured in a typical design year using a certain type of LID? or How many rain gardens are needed in a sewershed to capture a certain volume of storm water?A predictive model is presented to calculate the optimal number of rain gardens to achieve a target level of sewer overflow control.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".