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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.031 | 0.002 |
| 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 | 0.001 |
| 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".