Public health responses to toxic cyanobacterial blooms: perspectives from the 2016 Florida event
Bibliographic record
Abstract
Abstract In June 2016, massive cyanobacterial blooms occurred in the St. Lucie River in Florida, caused by nutrient and cyanobacterial-laden water releases from Lake Okeechobee. We independently collected and analyzed bloom material for cyanotoxin diversity and concentrations. The concentrations of microcystins, potent hepatotoxins, present in the bloom material greatly exceeded World Health Organization Guideline Values for drinking and recreational water. We also detected the neurotoxins anatoxin-a(S) and β-N-methylamino-L-alanine (BMAA). The Florida State Governor declared a state of emergency, but many affected aquatic recreational areas in St. Lucie County remained open during the bloom event without adequate hazard notification to citizens. During the bloom event, issues with preparedness, communication, sampling, analysis, closures and contingencies were observed. We suggest better ways that cyanobacterial bloom events can be predicted, managed, and mitigated in the future throughout the world. As similar problems with cyanobacterial bloom frequency and occurrence present worldwide, understanding governmental responses to the 2016 Florida incident can help in the development of effective mitigation and management strategies for future bloom events.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".