Comparison of Different Methods in Calculating CSS Percent Capture
Bibliographic record
Abstract
operates a combined sewer system (CSS) which includes 23 diversion cham-bers, eight pump stations, 12.6 mi (20 km) of interceptor sewers, and a wastewater treatment plant (WWTP). KVWPCA decided to use the United States Environmental Protection Agency (USEPA) Combined Sewer Overflow (CSO) Control Policy presump-tive approach Criterion 2 through their long term control plan (LTCP) process. Criterion 2 requires “The elimination or capture for treatment of no less than 85 % by volume of combined sewage collected in the CSS during precipitation events on a system-wide annual average basis. ” In order to assess the overflow volumes relative to total CSS conveyance on an annual average basis, KVWPCA completed a comprehensive flow monitoring, CSS hydrologic– hydraulic modeling study, and evaluated several methods to calculate their system percent capture. These calculation methods can be divided into two categories: the indirect method and the direct method. The indirect method first calculates the percent of flow loss (overflows and flooding) relative to total wet weather flow, and then deducts the percent loss from 100%. The direct method calculates the ratio of flow to the WWTP to the total wet weather flow during wet weather time. In either method, the determination of dry weather flow and wet weather time is critical. In order to assess the effect of different wet weather flow and time assumptions, different fixed and varied dry weather flow (DWF) thresh-olds were used. Also, in a large system like KVWPCA, which includes com-bined and separate sewersheds, the selection of pure combined system flow and mixed system flow also has significant influence on the calculated percent capture estimate.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".