Cyanobacteria toxins and the current state of knowledge on water treatment options: a review
Bibliographic record
Abstract
Cyanobacteria toxins have quickly risen in infamy as important water contaminants that threaten human health. This paper provides a broad overview of cyanobacteria toxins and the current state of knowledge about water treatment options to reduce these toxins. The first part of the paper focuses on cyanobacteria as organisms and their ability to produce a variety of toxins, the proposed or accepted regulatory guidelines for these toxins, and common detection techniques. Then a review is presented of the past 25 years worth of work on cyanobacteria toxin removal using both conventional and advanced water treatment processes and operations. The paper concludes by identifying directions for future research required to advance the abilities of utilities and water treatment plant designers to deal with these toxins while long-term, watershed management and surveillance plans are developed and implemented. As well, some suggestions are provided for immediate steps that a water utility facing cyanobacteria blooms could take to minimize human exposure to these toxins. Key words: cyanobacteria, blue-green algae, microcystin, cylindrospermopsin, cyanotoxins, water treatment, membrane filtration, advanced oxidation, UV photolysis, drinking water.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".