URBAN WET-WEATHER FLOWS: SOURCES OF FECAL CONTAMINATION IMPACTING ON RECREATIONAL WATERS AND THREATENING DRINKING-WATER SOURCES
Bibliographic record
Abstract
Discharges of urban stormwater and combined sewer overflows (CSOs) contribute to fecal contamination of urban waters and need to be considered in planning the protection of recreational waters and sources of drinking water. Stormwater characterization indicates that Escherichia coli counts in stormwater typically range from 103 to 104 units per 100 ml. Higher counts (10(5) units/100 ml) suggest the presence of cross-connections with sanitary sewers, and such connections should be identified and corrected. Fecal contamination of stormwater may be attenuated prior to discharge into surface waters by stormwater management measures, which typically remove suspended solids and attached bacteria. Exceptionally, stormwater discharges in the vicinity of swimming beaches are disinfected. The levels of indicator bacteria in CSOs can be as high as 10(6) E. coli per 100 ml. Consequently, the abatement of fecal contamination of CSOs is now considered in the design of CSO control and treatment, as for example stipulated in the Ontario Procedure F-5-5. CSO abatement options comprise combin ations of storage and treatment, in which the CSO treatment generally includes disinfection by ultraviolet (UV) irradiation. Finally, indicator bacteria data from Sarnia (Ontario) were used to demonstrate some fecal contamination impacts of wet-weather flows. In wet weather, the microbiological quality of riverine water worsened as a result of CSO and stormwater discharges, and the recreational water guidelines for indicator organisms were exceeded most of the time. Local improvements in water quality were feasible by source controls and diversion of polluted water.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".