Fecal source tracking and eDNA profiling in an urban creek following an extreme rain event
Bibliographic record
Abstract
Fecal contamination of recreational waters (i.e. lakes, rivers, beaches) poses an on-going problem for environmental and public health. Heavy rainfall can exacerbate existing problems with fecal contamination. As there could be variable sources of fecal contamination, identifying the source is critical for remediation efforts. This study utilized microbial source tracking (MST), chemical source tracking (CST) markers and environmental DNA (eDNA) metabarcoding to profile sampling areas and identify sources of fecal contamination in creek, stormwater outfall and beach sites in the Etobicoke Creek watershed (Toronto, ON). Water samples were collected before and immediately following an extreme rain event. MST and CST identified stormwater outfalls as an important source of human fecal contamination during dry and wet conditions. eDNA metabarcoding allowed for potential identification of additional sources of fecal contamination and provided additional evidence of human fecal contamination. The extreme rainfall event altered the eDNA profiles, causing creek and beach sites to reflect a greater diversity of mammal and bird eDNA sequences. The profiles provided by eDNA metabarcoding provide a proof of concept suggesting that eDNA metabarcoding can be a useful tool to complement MST and CST methods for profiling sources of fecal contamination and studying impacts of extreme rain events.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".