Microbial source tracking and spatial analysis of E. coli contaminated private well waters in southeastern Ontario
Bibliographic record
Abstract
Private water supplies, which are the primary source of drinking water for rural communities in developed countries, are at risk of becoming fecally contaminated. It is important to identify the source of contamination in order to better understand and address this human health risk. Microbial source tracking methods using human, bovine and general Bacteroidales markers were performed on 716 well water samples from southeastern Ontario, which had previously tested positive for Escherichia coli. The results were then geospatially analyzed in order to elucidate contamination patterns. Markers for human feces were found in nearly half (49%) of all samples tested, and a statistically significant spatial cluster was observed. A quarter of the samples tested positive for only general Bacteroidales markers (25.7%) and relatively few bovine specific marker positives (12.6%) were found. These findings are fundamental to the understanding of pathogen dynamics and risk in the context of drinking well water and will inform future research regarding host-specific pathogens in private well water samples.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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 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".