Microbial Source Tracking Using Quantitative and Digital PCR To Identify Sources of Fecal Contamination in Stormwater, River Water, and Beach Water in a Great Lakes Area of Concern
Bibliographic record
Abstract
Fecal contamination of recreational water poses a persistent and ongoing problem, particularly in areas of concern around the Great Lakes. The identification of the source(s) of fecal contamination is essential for safeguarding public health as well as guiding remediation efforts; however, fecal contamination may frequently be present at low levels and remain undetectable by certain methodologies. In this study, we utilized microbial source tracking techniques using both quantitative and digital PCR assays to identify sources of contamination. Our results indicated high levels of human fecal contamination within stormwater outfalls, while lower levels were observed throughout the watershed. Additionally, high levels of gull fecal contamination were detected at Rouge Beach, particularly during drier sampling events. Furthermore, our results indicated an increased sensitivity of the digital PCR assay to detect both human and gull contamination, suggesting it could be a viable tool for future microbial source tracking studies.
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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 | 0.000 | 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 | 0.001 |
| 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".