Quantitative microbial risk assessment of a drinking water – wastewater cross-connection simulation
Bibliographic record
Abstract
Quantitative microbial risk assessment is a useful way to predict the incidence of infection and illness within a community following exposure to pathogens. We used this risk assessment technique to determine the expected number of Salmonella infections and illnesses resulting from a drinking water – wastewater cross-connection incident using data generated from a distribution system simulator study and compared our results to a reported cross-contamination event that occurred in Pineville, Louisiana in 2000. Probabilities of infection and illness were estimated for different exposure scenarios representing different Salmonella concentrations and the characteristic varied attack rates for waterborne Salmonella. Risks of Salmonella infection range from 10% after a 1 day exposure (assuming the lower bound Salmonella concentration) to a 1-log greater risk of infection for all other scenarios, with risks of infection approximately 99% for 30 and 90 day exposure durations.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".