Unravelling sources of fecal pollution in oligotrophic mountain waters: Integrating Escherichia coli enumeration, microbial source tracking, and eDNA analysis
Bibliographic record
Abstract
Aquatic ecosystems in mountainous regions are crucial for fulfilling natural and anthropogenic water demands around the world. This study integrates Escherichia coli (E. coli) enumeration, microbial source tracking (MST), and environmental DNA (eDNA) analysis to identify sources of fecal contamination in oligotrophic mountain waters. Conducted in an area with intense tourism and traditional reindeer herding, this research addresses the urgent need to identify fecal pollution sources to safeguard the water quality of these vital ecosystems. Our study reveals that E. coli levels vary significantly across different locations and times, suggesting varied sources of contamination from humans, wildlife, and livestock animals. MST techniques, alongside eDNA analysis, provided insights into the complex patterns of fecal pollution, allowing for the distinction between human and animal contributions to water contamination. Our findings highlight the importance of combining various analytical methods to track fecal pollution sources effectively, and to develop targeted strategies for water quality management.
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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 | 0.000 |
| 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".