Toxic benthic freshwater cyanobacterial proliferations: Challenges and solutions for enhancing knowledge and improving monitoring and mitigation
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
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.
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 | 0.000 | 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) | 0.000 | 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 it