Review of Historical Street Dust and Dirt Accumulation and Washoff Data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it