Toxic Cyanobacteria: A Growing Threat to Water and Air Quality
Bibliographic record
Abstract
The global expansion of harmful cyanobacterial blooms (CyanoHABs) poses an increasing threat to public health. CyanoHABs are characterized by the production of toxic metabolites known as cyanotoxins. Human exposure to cyanotoxins is challenging to forecast, and perhaps the least understood exposure route is via inhalation. While the aerosolization of toxins from marine harmful algal blooms (HABs) has been well documented, the aerosolization of cyanotoxins in freshwater systems remains understudied. In recent years, spray aerosol (SA) produced in the airshed of the Laurentian Great Lakes (United States and Canada) has been characterized, suggesting that freshwater systems may impact atmospheric aerosol loading more than previously understood. Therefore, further investigation regarding the impact of CyanoHABs on human respiratory health is warranted. This review examines current research on the incorporation of cyanobacterial cells and cyanotoxins into SA of aquatic ecosystems which experience HABs. We present an overview of cyanotoxin fate in the environment, biological incorporation into SA, existing data on cyanotoxins in SA, relevant collection methods, and adverse health outcomes associated with cyanotoxin inhalation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".