Toxic benthic freshwater cyanobacterial proliferations: Challenges and solutions for enhancing knowledge and improving monitoring and mitigation
Bibliographic record
Abstract
1. This review summarises knowledge on the ecology, toxin production, and impacts of toxic freshwater benthic cyanobacterial proliferations. It documents monitoring, management, and sampling strategies, and explores mitigation options. 2. Toxic proliferations of freshwater benthic cyanobacteria (taxa that grow attached to substrates) occur in streams, rivers, lakes, and thermal and meltwater ponds, and have been reported in 19 countries. Anatoxin- and microcystin-containing mats are most commonly reported (eight and 10 countries, respectively). 3. Studies exploring factors that promote toxic benthic cyanobacterial proliferations are limited to a few species and habitats. There is a hierarchy of importance in environmental and biological factors that regulate proliferations with variables such as flow (rivers), fine sediment deposition, nutrients, associated microbes, and grazing identified as key drivers. Regulating factors differ among colonisation, expansion, and dispersal phases. 4. New -omics-based approaches are providing novel insights into the physiological attributes of benthic cyanobacteria and the role of associated microorganisms in facilitating their proliferation. 5. Proliferations are commonly comprised of both toxic and non-toxic strains, and the relative proportion of these is the key factor contributing to the overall toxin content of each mat. 6. While these events are becoming more commonly reported globally, we currently lack standardised approaches to detect, monitor, and manage this emerging health issue. To solve these critical gaps, global collaborations are needed to facilitate the rapid transfer of knowledge and promote the development of standardised techniques that can be applied to diverse habitats and species, and ultimately lead to improved management.
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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 | 0.018 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.017 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".