Perceived global increase in algal blooms is attributable to intensified monitoring and emerging bloom impacts
Bibliographic record
Abstract
Global trends in the occurrence, toxicity and risk posed by harmful algal blooms to natural systems, human health and coastal economies are poorly constrained, but are widely thought to be increasing due to climate change and nutrient pollution. Here, we conduct a statistical analysis on a global dataset extracted from the Harmful Algae Event Database and Ocean Biodiversity Information System for the period 1985-2018 to investigate temporal trends in the frequency and distribution of marine harmful algal blooms. We find no uniform global trend in the number of harmful algal events and their distribution over time, once data were adjusted for regional variations in monitoring effort. Varying and contrasting regional trends were driven by differences in bloom species, type and emergent impacts. Our findings suggest that intensified monitoring efforts associated with increased aquaculture production are responsible for the perceived increase in harmful algae events and that there is no empirical support for broad statements regarding increasing global trends. Instead, trends need to be considered regionally and at the species level.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".