Environmental influences and prediction of Escherichia coli concentration in freshwater recreational beaches in Southern Ontario
Bibliographic record
Abstract
Background and Aim: The concentration of Escherichia coli is used in Canada as an indicator of fecal pollution in freshwater recreational beaches, and is associated with recreational water illness among beachgoers. This study examines the environmental predictors of E. coli in Toronto and Niagara Region beaches to inform beach monitoring programs and reduce beachgoer illness risks. Methods: Our first objective used advanced analytical methods to examine region-specific environmental predictors of E. coli at 18 beaches in Toronto and Niagara Region. Mixed-effects models investigated regional differences, while the application of path analysis identified intervariable relationships and pathways associated with E. coli in Niagara Region. Our second objective involves the development of region-specific predictive models using a novel Bayesian Network approach to provide real-time assessments of beach water E. coli concentrations in our study regions. Results: E. coli observations were collected from 2007-2019 for Toronto and 2011-2019 for Niagara Region. In the mixed-effects analysis, substantial clustering of E. coli values at the beach level was observed in Toronto, while minimal clustering was seen in Niagara, suggesting an important beach-specific effect in Toronto beaches. Air temperature and turbidity were positively associated with E. coli in all models in both regions. In the path analysis, we found that water turbidity was an important mediator for the indirect effect of environmental variables overall and in beach-specific models. Results from these analyses informed the development of region-specific Bayesian Network predictive models, which are currently being tested and finalized. Conclusions: Poor beach water quality could result in an increased risk of recreational water illness among the beachgoers. The development of accurate predictive models will guide beach managers in decision-making and risk communication to reduce recreational water illness risks among beachgoers. Keywords: E. coli, water quality, recreational water illness
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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) | 0.004 | 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".