Public health responses to toxic cyanobacterial blooms: perspectives from the 2016 Florida event
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.005 |
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 it