Stormwater Biofilter Media Performance Updates for WinSLAMM: Data Summary for Unit Process Components, Example Calculations and Model Use
Bibliographic record
Abstract
This paper focuses on specific information needed to model various aspects of the pollutant retention processes in stormwater biofilters. Updates currently being incorporated in WinSLAMM (Source Loading and Management Model) are building on expanded data from laboratory and field research mostly conducted by Pitt’s research group at the University of Alabama, Tuscaloosa, by Dr. Shirley Clark’s research group at Penn State–Harrisburg, and the Wisconsin Department of Natural Resources and the U.S. Geological Survey (DNR/USGS). These processes and data can be used in manual calculations or other models. Extensive summaries of these data sources and associated statistical analyses, plus additional references, are included in an online white paper available at the PV & Assoc. website. These tests were conducted to provide the details needed for modeling the performance of biofilters, specifically focusing on: methods to predict treatment flow rates through the media; particulate and associated particulate bound pollutant retention for several particle sizes; maintenance requirements due to sediment clogging and pollutant breakthrough; and retention of filterable pollutants. This multiyear research program also examined issues not commonly described in the biofilter performance literature, such as failure due to excessive salt loadings on media having large amounts of fines, problems associated with compaction of the media, and leaching of previously captured material from the media.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.025 |
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".