Pathogen Intrusion in Distribution Systems: Model to Assess the Potential Health Risks
Bibliographic record
Abstract
A model for estimating the probability of infection from intrusion events associated with low/negative pressure occurrences in distribution system is presented. The modeling approach, based on the principle of quantitative microbial risk assessment, predicts infection rates as a function of several parameters: the orifice equation (for calculation of intrusion flow rate), the external contaminant concentration, the starting time, the duration of low/negative pressures, the location and extent of intrusion area, the hydraulic and operational conditions in the distribution system, consumption events at fixed-times and dose-response information for specific microorganisms. The approach combines the use of a probabilistic model to determine the possible range of contaminant mass rates that could be encountered and the use of a hydraulic model to determine population exposure to contaminated water once an intrusion event has taken place. Using a model distribution system (EPANET Example Network 2), the effects of intrusion event characteristics (starting time, duration, location, contaminant mass rate) on the probability for an healthy adult of being infected by Cryptosporidium from sewage contamination of the distribution system were investigated. Based on the current model assumptions, results show that the risk of infection may vary over several orders of magnitude depending upon where the water is consumed and the intrusion event characteristics.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".