Bibliographic record
Abstract
The combined sewer overflow (CSO) control requirements imposed by the United States Environmental Protection Agency (USEPA) on approximately 1 100 sewer district utilities within the United States initially focused solely on the volumetric reduction of CSOs, with the assumption that a corresponding reduction in pollutant loads to a combined sewer system's (CSS) receiving stream would result.As the development of CSO long term control plans for addressing the agency's CSO control policies has progressed, the focus of CSO control has shifted, and assessing water quality benefits through quantitative analysis is becoming more common.Development of CSO improvements typically involves the consideration of several alternatives, and the benefits provided by each are evaluated in addition to its cost.While evaluating CSO control alternatives in Cincinnati, Ohio a simplified approach for comparing the relative water quality benefits achieved by each alternative was developed.Pollutant load event mean concentrations (EMCs) were developed for the pollutants of concern, based on available national average information.Within the existing conditions and alternatives models being evaluated utilizing USEPA's Stormwater Management Model 5 (SWMM5), EMC assignments were applied to the rainfall derived infiltration and inflow, sanitary baseflow and individual subareas based on land use characteristics.The treatment effectiveness of both grey and green CSO and stormwater treatment facilities were simulated using estimated pollutant removal efficiencies.A single design storm event as well as continuous annual simulation modeling over an entire year was performed using design storm rainfall and historical rainfall data.Pollutant loadings to the receiving stream were quantified and compared to assess the water quality benefit of each alternative.This study presents these results and provides an approach for making relative comparisons of the water quality benefits offered by CSO control alternatives within any CSS.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".