Identification of fecal contamination sources in water using host-associated markers
Bibliographic record
Abstract
In British Columbia, Canada, drinking water is tested for total coliforms and Escherichia coli, but there is currently no routine follow-up testing to investigate fecal contamination sources in samples that test positive for indicator bacteria. Reliable microbial source tracking (MST) tools to rapidly test water samples for multiple fecal contamination markers simultaneously are currently lacking. The objectives of this study were (i) to develop a qualitative MST tool to identify fecal contamination from different host groups, and (ii) to evaluate the MST tool using water samples with evidence of fecal contamination. Singleplex and multiplex polymerase chain reaction (PCR) were used to test (i) water from polluted sites and (ii) raw and drinking water samples for presence of bacterial genetic markers associated with feces from humans, cattle, seagulls, pigs, chickens, and geese. The multiplex MST assay correctly identified suspected contamination sources in contaminated waterways, demonstrating that this test may have utility for heavily contaminated sites. Most raw and drinking water samples analyzed using singleplex PCR contained at least one host-associated marker. Singleplex PCR was capable of detecting host-associated markers in small sample volumes and is therefore a promising tool to further analyze water samples submitted for routine testing and provide information useful for water quality management.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".