Effects of lake warming on the seasonal risk of toxic cyanobacteria exposure
Bibliographic record
Abstract
Abstract Incidence of elevated harmful algal blooms and concentrations of microcystin are increasing globally as a result of human‐mediated changes in land use and climate. However, few studies document changes in the seasonal and interannual concentrations of microcystin in lakes. Here, we modeled 11 yr of biweekly microcystin data from six lakes to characterize the seasonal patterns in microcystin concentration and to ascertain if there were pronounced changes in the patterns of potential human exposure to microcystin in lakes of central North America. Bayesian time series analysis with generalized additive models found evidence for a regional increase in microcystin maxima and duration but recorded high variation among lakes. During the past decade, warmer temperatures, but not nutrient levels, led to a marked increase in the number of days when concentrations exceeded drinking and recreational water thresholds set by the World Health Organization and United States Environmental Protection Agency.
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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".