Upflow Filtration for the Treatment of Stormwater at Critical Source Areas
Bibliographic record
Abstract
One approach to the treatment of urban runoff is to treat the runoff from critical source areas before it mixes with runoff from less pollutant areas.Some of the general features of critical source areas appear to be large paved areas, heavy vehicular traffic, and/or exposed heavy equipment, materials or products.The control of runoff from relatively small critical source areas (such as loading docks, fueling areas, small maintenance yards, etc.) may be the most cost effective approach for the treatment/reduction of stormwater toxicants.However, in order for a treatment device to be usable, it must be inexpensive, both to purchase and to maintain, and be effective.Upflow filtration of stormwater was tested during both controlled tests, and under actual rainfall conditions, during SBIR1 (Small Business Innovative Research) and SBIR2 research funded by the US EPA.This chapter summarizes the work presented by Pitt, et al. (2005), Khambhammettu (2006), andPitt, et al. (2006) reporting on this research.Upflow filtration was originally developed to overcome some of the problems associated with conventional filtration.The most serious problem is that downflow filters clog relatively quickly, reducing the treatment flow
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".