A USEPA SWMM Integrated Tool for Determining the Suspended Solids Reduction Performance of Bioretention Cells
Bibliographic record
Abstract
A numerical tool was developed to calculate the stormwater runoff total suspended solids (TSS) removal efficiency of bioretention cells to assist engineers in obtaining credit and approval for bioretention cell facilities.Numerical models for filtration were used in developing this tool, as they have previously been successfully used for bioretention cells.The equations were adapted to integrate with the widely used USEPA SWMM, through its Add-in Tools feature.The tool was first tested to ensure the model matched the monitored performance of a bioretention cell and, second, benchmarked against the TSS removal predicted by another modeling tool (WinSLAMM).The capability of the tool to accurately simulate the TSS reduction performance of the monitored bioretention cell supports its suitability for use in designing bioretention facilities.This research, model development, and verification are the first steps towards the complete development of a stormwater runoff TSS removal model capable of continuous simulation, which will aid in bioretention cell design and installation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".