Quantitative microbial risk assessment for drinking water intake threat prioritization: a comparison of vulnerability and threat assessment according to source water protection regulations of two Canadian provinces
Bibliographic record
Abstract
Source Water Protection in Canada is regulated primarily by provincial governments, leading to a variety of approaches for characterizing threats to drinking water. This paper compares the key elements of vulnerability and threat assessments for microbial contaminants for two Canadian provinces. Drinking water intakes of two municipalities in Quebec and Ontario, Canada, located on opposite sides of a large transboundary river impacted by Combined Sewer Overflow (CSO) discharges were used as a case study to evaluate the two provincial approaches. Québec’s vulnerability classification for microbial contaminants is data driven based on regulatory monitoring (concentrations of Escherichia coli ) at the drinking water intake) while that of Ontario’s is model driven and dependent on the physical and hydraulic characteristics of zones around an intake. To establish a quantitative criterion to compare these two threat assessment frameworks, the impacts of a series of CSO events upstream of the drinking water intakes were simulated using a calibrated hydrodynamic and water quality model. Corresponding enteric pathogen concentrations in the intakes were estimated and used as input for Quantitative Microbial Risk Assessment (QMRA) to calculate treatment requirement levels to meet human health targets. Unlike Ontario’s threat assessment approach, Quebec’s approach provides an opportunity to investigate the effectiveness of risk reduction strategies such as an adjustment of the frequency of CSO events or corrective actions to improve treatment. Considering the influence of CSO events on log removal requirements to remain compliant with human health targets permitted the differentiation of CSO risk levels for threat prioritization.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".