Climate change amplifies the risk of potentially toxigenic cyanobacteria
Bibliographic record
Abstract
Cyanobacterial blooms pose a significant threat to water security, with anthropogenic forcing being implicated as a key driver behind the recent upsurge and global expansion of cyanobacteria in modern times. The potential effects of land-use alterations and climate change can lead to complicated, less-predictable scenarios in cyanobacterial management, especially when forecasting cyanobacterial toxin risks. There is a growing need for further investigations into the specific stressors that stimulate cyanobacterial toxins, as well as resolving the uncertainty surrounding the historical or contemporary nature of cyanobacterial-associated risks. To address this gap, we employed a paleolimnological approach to reconstruct cyanobacterial abundance and microcystin-producing potential in temperate lakes situated along a human impact gradient. We identified breakpoints (i.e., points of abrupt change) in these time series and examined the impact of landscape and climatic properties on their occurrence. Our findings indicate that lakes subject to greater human influence exhibited an earlier onset of cyanobacterial biomass by 40 years compared to less-impacted lakes, with land-use change emerging as the dominant predictor. Moreover, microcystin-producing potential increased in both high- and low-impact lakes around the 1980s, with climate warming being the primary driver. Our findings chronicle the importance of climate change in increasing the risk of toxigenic cyanobacteria in freshwater resources.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".