State approaches to addressing cyanotoxins in drinking water
Bibliographic record
Abstract
Cyanobacterial blooms present a risk to water supplies, especially in nutrient‐enriched bodies of water. Some algal blooms can produce cyanotoxins at levels of concern for human health and aquatic ecosystems. Currently no federally enforceable limits exist for microcystins, cylindrospermopsin, or any other cyanotoxins. However, several states have taken action based on nonenforceable U.S. Environmental Protection Agency's (USEPA's) health advisories and their own processes. This study assessed the status and scope of those state‐level programs. Data were collected through interviews with state regulatory officials accompanied by publicly available information. The authors contacted officials in each of the 50 U.S. states. Forty‐six provided responses, and four have only publicly available information. Twenty‐nine states reported to have already developed or are currently developing guidance, while 13 indicated that cyanotoxins are not an issue of concern within their jurisdiction. Two states appear in both categories. The statuses of the remaining 10 states' programs fall in between.
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.
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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".