Application of high-throughput 16S rRNA sequencing to identify fecal contamination sources and to complement the detection of fecal indicator bacteria in rural groundwater
Bibliographic record
Abstract
Residents in rural communities across Canada collect potable water from aquifers. Fecal contaminants from sewage and agricultural runoffs can penetrate aquifers, posing a public health risk. Standard methods for detecting fecal contamination test for fecal indicator bacteria (FIB), but the presence of these do not identify sources of contamination. In contrast, DNA-based diagnostic tools can achieve this important objective. We employed quantitative polymerase chain reaction (qPCR) and high-throughput DNA sequencing to trace fecal contamination sources in Wainfleet, a rural Ontario township that has been under the longest active boil water advisory in Canada due to FIB contamination in groundwater wells. Using traditional methods, we identified FIBs indicating persistent fecal pollution in well waters. We used 16S rRNA sequencing to profile groundwater microbial communities and identified Campylobacteraceae as a fecal contamination DNA marker in septic tank effluents (STEs). We also identified Turicibacter and Gallicola as a potential cow and chicken fecal contamination marker, respectively. Using human specific Bacteroidales markers, we identified leaking septic tanks as the likely primary fecal contamination source in some of Wainfleet's groundwater. Overall, the results support the use of sequencing-based methods to augment traditional water quality testing methods and help end-users assess fecal contamination levels and identify point and non-point pollution sources.
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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 | 0.002 | 0.000 |
| 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 | 0.000 |
| 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 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".