Environmental factors associated with freshwater recreational water quality in Niagara Region, Ontario, Canada: A path analysis
Bibliographic record
Abstract
Abstract Escherichia coli concentration levels in recreational water are used by beach managers to evaluate the risk of gastrointestinal illness among beachgoers. We examined the relationship between specific environmental factors and E. coli concentration in recreational beaches in the Niagara Region. We analysed E. coli geometric means collected from eight beaches from two of the Great Lakes in the Niagara Region in Ontario, between 2011 and 2019. We applied path analysis to evaluate the relationship between the environmental factors and E. coli concentrations, including whether effects were direct or indirect via a mediator. Turbidity was found to be an important mediator for the indirect effect of environmental variables overall and in beach-specific models. Rainfall and streamflow had a positive indirect effect on E. coli via turbidity and a direct effect in five out of seven beach models. Streamflow was also a mediator for the indirect effect of previous day air temperature in five out of seven models. In three subset models, outfall E. coli concentration was a mediator for the effect of the environmental factors. Using a novel methodological approach, this study identifies important relationships and pathways that predict beach E. coli concentration in freshwater beaches located on two of the Great Lakes.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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 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".