Perceptions of freshwater algal blooms, causes and health among New Brunswick lakefront property owners
Bibliographic record
Abstract
Abstract Changes to water conditions due to eutrophication and climate change have resulted in the proliferation of harmful algal blooms in freshwater and marine environments globally, including in Canadian lakes. We developed and administered an online survey to evaluate the awareness of these blooms and the accompanying health risks in a sample of New Brunswick waterfront cottage and homeowners. The survey was distributed to lake and cottage associations in New Brunswick and was completed by 186 eligible respondents. Participants were asked to about information about the water quality of their lake, awareness about algae blooms, sociodemographic and cottage characteristics, and complete a self-rated measure of physical and mental health. While approximately 75% of participants reported that the quality of their lake water was good or very good, 40% indicated that algae blooms were a concern. We found no statistically significant differences in self-reported physical or mental health between those who were aware of algae blooms at their cottage and those who weren’t (p > 0.05). Participants expressed concern about the impacts of algal blooms on the health of their pets, and wildlife. While climate change was the most identified cause of algal blooms, there was substantial heterogeneity in the responses. Taken together, the findings from our survey suggest that cottage owners in New Brunswick are aware and concerned about the impacts of algae blooms, however, there is a need to provide additional information to lake associations about the causes of these blooms.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".