The Role of Pollution Prevention in Stormwater Management
Bibliographic record
Abstract
Around the nation, there is growing interest in the development and use of environmentally sensitive construction materials as a low-cost component to stormwater management It is thought that the more appropriate selection of materials that are exposed to the environment should result in significant reductions of many toxicants in stormwater.Unfortunately, there is little data for specific altemative building materials, although much information exists targeting selected sources, especially the role of roof runoff as a significant source of zinc and other metals.Past studies have identified mban runoff as a major contributor to the degradation of many urban streams and rivers (such as Field and Tmkeltaub 1981;Pitt and Bozeman, 1982;Pitt and Bissonnette, 1984;Pitt, 1995).Previous studies also found organic and metallic toxicants in urban storm-induced discharges that can contribute to receiving water degradation (such as EPA, 1983; Hoffman et al., 1984; Fram et al., 1987).Studies conducted by Pitt et al. (1995 and2000) investigated toxic contributions to mban wet weather flow from sources such as roofs, parking areas, storage areas, streets, loading docks, vehicle service areas, and landscaped areas.Roof, vehicle service area and parking lot runoff samples were found to have the greatest organic toxicant detection frequencies and the highest levels of detected metals.Research is currently underway at the University of Alabama (UAB) to develop effective procedmes for treating runoff from vehicle service areas and parking lots at its source (Clark and Pitt 1999;Pitt et al., 2000).These areas are particularly subject to spills and leaks of automotive products and exhaust emissions from Pitt, R.E. and M. Lalor.2001."The Role of Pollution Prevention in Stormwater Management."
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".