Monitoring, Managing, and Communicating Risk of Harmful Algal Blooms (HABs) in Recreational Resources across Canada
Bibliographic record
Abstract
Globally, harmful algal blooms (HABs) are on the rise, as is evidence of their toxicity. The impacts associated with blooms, however, vary across Nation states, as do the strategies and protocols to assess, monitor, and manage their occurrence. In Canada, water quality guidelines are standardized nationally, but the management strategies for HABs are not. Here, we explore current strategies to understand how to better communicate risks associated with HABs to the public. Our team conducted an environmental scan on provincial and territorial government agency protocols around HABs. Results suggest that there are variations in the monitoring, managing, and communicating of risk to the public: British Columbia, Manitoba, New Brunswick, and Quebec have well-established inter-agency protocols, and most provinces report following federal guidelines for water quality. Notably, 3 northern territories have no HABs monitoring or management protocols in place. More populous provinces use a variety of information venues (websites, social media, on site postings, and radio) to communicate risks associated with HABs, whereas others' communications are limited. To induce more collaboration on HABs monitoring and management and reduce the associated risks, creating a coherent system with consistent messaging and inter-agency communication is suggested.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".