Burden of recreational water illness due to exposure to cyanobacteria and their toxins in freshwater beaches in Canada: protocol of a prospective cohort study
Bibliographic record
Abstract
INTRODUCTION: Cyanobacterial blooms are increasingly common in freshwater sources used for swimming and other recreational water contact activities in Canada. Many species of cyanobacteria can produce toxins that affect human and animal health, but there are limited data on the risk of illness associated with water contact at impacted beaches. METHODS AND ANALYSIS: This study will investigate the incidence of recreational water illness due to exposure to cyanobacterial blooms and their toxins in four targeted and popular freshwater beaches in Ontario, Manitoba and Nova Scotia, Canada. A prospective cohort design and One Health approach will be used. On-site recruitment of recreational water users will be conducted at two beaches per year during the summers of 2024 and 2025. The population of interest includes recreational water users of any age and their pet dogs. After enrolment, an in-person survey will determine beach exposures and confounding factors, and a 3-day follow-up survey will ascertain any acute illness outcomes experienced by participants or their dogs. The target sample size is 2500 recreational water users. Water samples will be taken each recruitment day and analysed for cyanobacterial indicators (pigments), cell counts and toxin levels. Bayesian regression analysis will be conducted to estimate the association with water contact, cyanobacterial levels and risks of different acute illness outcomes. ETHICS AND DISSEMINATION: This study has been approved by the Toronto Metropolitan University Research Ethics Board (REB 2023-461). Study results will be published in a peer-reviewed journal and as infographics on a project website.
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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 | 0.011 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".