Review of Historical Street Dust and Dirt Accumulation and Washoff Data
Bibliographic record
Abstract
Many complex models that utilize continuous simulation (SWMM, HSPF, SLAMM, SIMPTM, etc.) require information pertaining to the accumulation rate of pollutants on the land surfaces.This is one of the most perplexing issues in stormwater modeling.A representation of the accumulation rates is usually obtained through trial and error during calibration, with little, if any, actual direct measurements.Historically, direct measurements have been misapplied in modeling applications, resulting in unreasonable model predictions.Many modelers therefore forego accumulation rate data, preferring to back into values from outfall observations.This approach makes it very difficult to correctly predict the sources of stormwater pollutants in urban areas and to make reasonable stormwater management decisions using source area controls.This dilemma has come about due to a major misinterpretation of previously collected field data: the assumption that street dirt loadings are zero after most rains.With the correct understanding and modeling of the washoff process, the vast amount of historically collected accumulation data becomes an important modeling resource.This Chapter presents a summary of this useful information.This information has been used in Pitt and Voorhees' Source Loading and Management Model (SLAMM) and variations have been used in Sutherland's Simple Particulate Transport Model (SIMPTM) to more accurately predict these important source area processes.Relatively simple modifications can be made to other continuous models that utilize accumulation and washoff functions for more accurate and complete stormwater control predictions.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".