A public health perspective on cyanobacteria, climate change, population growth, and potential risk of neurodegenerative disease
Bibliographic record
Abstract
Abstract Cyanobacteria are a global health issue. These bacteria can produce toxins (cyanotoxins) that have important health consequences and exposure can occur through multiple pathways including drinking water and seafood. While several cyanotoxins are regulated and monitored, most are not. In this perspective, we provide a narrative review describing how climate change and population growth contribute to increasing risk of cyanotoxin exposure. Next, we explore the hypothesis that cyanobacteria are associated with the development of neurodegenerative diseases. Through synthesizing this literature, we underscore the need for more exposure data to elucidate specific health effects. To demonstrate this gap and understand what data are available in British Columbia, Canada (BC), we describe the current approach to monitoring cyanobacteria and cyanotoxins in BC lakes. We identify sources of data and knowledge gaps in this BC case study that would inform exposure and exposure-health effect studies, including understanding links between cyanotoxins and neurodegenerative diseases. We find that although exposure risk is likely to be heterogenous, monitoring across BC lakes is disparate, making long-term, provincial-scale surveillance difficult. We discuss this case study with a view towards identifying opportunities in BC for performing future research and as an example for others seeking to understand the risks in their regions.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".