Occurrence, toxins and possibilities of control of bloom-forming cyanobacteria of European freshwaters: a review
Bibliographic record
Abstract
Blooming of cyanobacteria is a common problem of eutrophic water bodies in Europe and worldwide and can cause severe problems with toxicity, taste and odour of the water. Toxins produced by cyanobacteria (cyanotoxins) are structurally diverse and their effects range from liver damage, including liver cancer, to neurotoxicity and thus they may present a serious threat for drinking water safety. Cyanobacterial blooms present major challenges for the management of rivers, lakes and reservoirs and are predicted to cause even worse problems in the future due to the climate change associated with global warming, increased availability of light to phytoplankton and rising levels of atmospheric CO2. This paper presents the literature review of occurrence, toxins (along with their effects on human health) and possibilities of control of bloom-forming cyanobacteria.
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How this classification was reachedexpand
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) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".