Occurrence and predictive correlations of Escherichia coli and Enterococci at Sandpoint beach (Lake St Clair), Windsor, Ontario and Holiday beach (Lake Erie), Amherstburg, Ontario
Bibliographic record
Abstract
Point and nonpoint sources contribute to fecal contamination of surface waters by human pathogens that exist at low concentrations and are difficult, expensive, and/or impossible to easily detect. Therefore, fecal indicator bacteria (FIB) are used as surrogates in identification of fecal contamination, including Escherichia coli (EC) and Enterococcus spp. (ENT), at recreational beaches to protect from adverse health effects from exposure to water-borne pathogens. The objective of the current study was to conduct a preliminary investigation of the environmental processes contributing to the nature and significance of FIB (EC and ENT) over 30 d at Sandpoint beach (Windsor, Ontario) and Holiday beach (Amherstburg, Ontario). Daily, three 100 mL samples were collected for EC and ENT for analysis by Colilert® and Entrolert®, respectively. Additionally, physicochemical and hydrometerological data were measured or taken from data archives. Both EC and ENT populations were dynamic and well correlated to each other (p < 0.05; analysis of variance (ANOVA)) and both FIB correlated with turbidity and wave height (p < 0.10; ANOVA). Despite being geographically close and therefore having similar meteorological data, both beaches exhibited markedly different FIB, turbidity and wave height data, suggesting that beach-specific data should be considered for any future predictive applications.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".