URBAN WET-WEATHER FLOWS: SOURCES OF FECAL CONTAMINATION IMPACTING ON RECREATIONAL WATERS AND THREATENING DRINKING-WATER SOURCES
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it