Exploring private water wells for fecal sources and evidence of pathogen presence in the context of current testing practices for potability in Ontario
Bibliographic record
Abstract
Abstract Private water wells provide drinking water for an estimated 4.1 million households in Canada yet remain understudied in the context of microbial water quality or human health impacts. As there exists little systematic surveillance for enteric infections or outbreaks related to well water sources, consumers may be at risk of waterborne infectious diseases. A standard protocol in Ontario requires 200 mL of water, collected, and submitted by well owners, half of which is used to analyze for Escherichia coli and total coliforms (TCs). The aim of this study was to determine the efficacy of testing small water volumes and to survey for other contaminants in addition to bacterial indicators to inform pathogen prevalence and fecal source in drinking water wells. Samples were assessed for E. coli and TCs, by culture, and genetic markers of Bacteroides spp., Campylobacter spp., Salmonella spp., and Shiga toxin-producing E. coli, using qPCR. The source of fecal contamination varied by the geographic region and may be explained by septic tank density and underlying geology, among other factors. A small number of samples (1.9%) showed the evidence of contamination with enteric pathogens. Lastly, E. coli measured by qPCR, as opposed to culture, correlated more strongly to Bacteroides markers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".