Microbial water quality at contrasting recreational areas in a mixed-use watershed in eastern Canada
Bibliographic record
Abstract
Recreational water use is an important source of human enteric illness. Enhanced (episodic) surveillance of natural recreational waters as a supplement to beach monitoring can enrich our understanding of human health risks. From 2011 to 2013, water sampling was undertaken at recreational sites on a watershed in eastern Canada. This study compared the prevalence and associations of human enteric pathogens and fecal indicator organisms. Beach water samples had lower pathogen presence than those along the main river, due to different pollution sources and the hydrological disposition. Pathogen profiles identified from the beach sites suggested a more narrow range of sources, including birds, indicating that wild bird management could help reduce public health risks at these sites. The presence and concentration of indicator organisms did not differ significantly between beaches and the river. However, higher concentrations of generic Escherichia coli were observed when Salmonella and Cryptosporidium were present at beach sites, when Salmonella was present at the river recreational site, and when verotoxigenic E. coli were present among all sites sampled. In this watershed, generic E. coli concentrations were good indicators of potential contamination, pathogen load, and elevated human health risk, supporting their use for routine monitoring where enhanced pathogen testing is not possible.
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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 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".