A genomic approach to microbial source tracking of fecal indicator bacteria in recreational surface waters (589.2)
Bibliographic record
Abstract
We present results of a multi‐year study to identify sources of chronic high fecal indicator bacterial levels in the Lake Macatawa (Ottawa County, Michigan) watershed. In this work, we have analyzed community bacterial DNA from environmental samples to characterize total microbial community populations using traditional qPCR and next generation DNA sequencing and analysis techniques. Standard microbiological and 16S rRNA qPCR source tracking techniques have revealed widespread and possibly endogenous nonpoint sources, as well as occasional point source hotspots. These results are compared to microbial community source tracking is based on DNA information in the same marker molecule, 16S rRNA, with the community source tracking approach allowing for the monitoring all microbial groups in the same samples. The baseline information obtained on microbial communities in the watershed has facilitated development of unique microbial profiles for possible point sources and perform qPCR based microbial source tracking that is robust to false positives and false negatives. Results of suites of newly described indicator bacteria that reflect various environmental events and their associate health and environmental risks will be discussed.
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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 | 0.000 | 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) | 0.001 | 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".