Assessing fecal pollution source in a Northern Michigan Lake using qPCR and a community-based monitoring framework
Bibliographic record
Abstract
Implementing quantitative polymerase chain reaction (qPCR) within a community-based research framework expands the scope and scale of community-driven monitoring and research efforts. The increasing accessibility of qPCR technology and methodology has allowed the incorporation of community partners in numerous ways, ranging from sample collection to running qPCR tests. Here, we report on a community-driven study at Crystal Lake in Beulah, MI, in which qPCR was demonstrated to be a more valuable water testing technique than culture-based methods. Historically high levels of the enteric bacteria Escherichia coli in the inlet to Crystal Lake, Cold Creek, as measured by culture-based testing methods, spurred an interest in understanding more about fecal pollution and its source. In this study, we monitored 17 sites in Cold Creek and around Crystal Lake throughout the summers of 2020 and 2021 and used qPCR to assess levels of Enterococcus while source-tracking all samples for human, dog, and Canada goose fecal markers (HF183, DG3 and CG0F1-Bac, respectively). Replicate samples were sent for E. coli culture-based testing. Results showed high fecal contamination (E. coli and Enterococcus) and consistent HF183, DG3 and CG0F1-Bac-positive samples at specific sample sites. Varying degrees of relatedness were found between Enterococcus levels grouped by precipitation amount. Due to the nature of the sampling sites, we hypothesize that human fecal contamination is due to stormwater outflows and septic system influences and not direct human contact with the water. A Cohen's Kappa analysis between the Enterococcus qPCR test results and E. coli culture-based test results indicated a moderately positive relationship. The historical E. coli dataset, now accompanied by the Enterococcus, HF183, DG3 and CG0F1-Bac data, confirms consistent and elevated levels of fecal pollution in Cold Creek and Crystal Lake that is likely related to human sources with stormwater outflows being a contributor to this contamination.
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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 | 0.001 |
| 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 | 0.001 |
| 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".