Pressure Monitoring and Characterization of External Sources of Contamination at the Site of the Payment Drinking Water Epidemiological Studies
Bibliographic record
Abstract
The 1990s epidemiological studies by Payment and colleagues suggested that an increase in gastrointestinal illnesses observed in the population consuming tap water from a system meeting all water quality regulations might be associated with distribution system deficiencies. In the current study, the vulnerability of this distribution system to microbial intrusion was assessed by characterizing potential sources of contamination near pipelines and monitoring the frequency and magnitude of negative pressures. Bacterial indicators of fecal contamination were recovered more frequently in the water from flooded air-valve vaults than in the soil or water from pipe trenches. The level of fecal contamination in these various sources was more similar to levels from river water rather than wastewater. Because of its configuration, this distribution system is vulnerable to negative pressures when pressure values out of the treatment plant reach or drop below 172 kPa (25 psi), which occurred nine times during a monitoring period of 17 months. The results from this investigation suggest that this distribution system is vulnerable to contamination by intrusion. Comparison of the frequency of occurrence of negative pressure events and repair rates with data from other distribution systems suggests that the system studied by Payment and colleagues is not atypical.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".