Microbial source tracking of private well water samples across at-risk regions in southern Ontario and analysis of traditional fecal indicator bacteria assays including culture and qPCR
Bibliographic record
Abstract
Many people living in rural areas rely on privately owned wells as their primary source of drinking water. These water sources are at risk for fecal contamination of human, wildlife, and livestock origin. While traditional bacteriological testing involves culture-based methods, microbial source tracking (MST) assays present an opportunity to additionally determine the source of fecal contamination. This study investigated the main host sources of contamination in private well water samples with high levels of Escherichia coli (E. coli), using MST with human and multi-species specific markers. Fecal contamination of human origin was detected in approximately 50% of samples, indicating that current contamination prevention strategies require reconsideration. The relationship between cattle density and fecal contamination of bovine origin was investigated using a Bovine Bacteroidales specific MST assay. Regional variations of microbial sources were examined, and may inform local primary prevention strategies. Additionally, in order to assess MST and E. coli quantitative real time polymerase chain reaction (qPCR) assays as indicators of fecal contamination, these were compared to E. coli culture methods. Variation in results was observed across all assay methods investigated, suggesting the most appropriate routine bacteriological testing methodology cannot be determined without comparison to a method that directly detects the presence of fecal contamination.
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 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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".