Distribution and impacts of Harmful Algal Blooms in the ICES area
Bibliographic record
Abstract
No abstracts are to be cited without prior reference to the author. Harmful Algal Blooms (HABs) represent a major hazard for the exploitation of coastal resources in ICES countries. Blooms of toxin-producing (low biomass) HABs are recurrent throughout the whole ICES region leading to prolonged shellfish harvesting bans when regulatory levels are exceeded; fishkilling (high biomass) HABs affect intensive caged-fish aquaculture in Scandinavia, Scotland and western Canada. Emerging benthic HABs have caused isolated events of Ciguatera Fish Poisoning (CFP) in Macaronesia (Canary, Madeira Islands) and outbreaks of toxic sea-spray on Mediterranean beaches. In the Baltic Sea, cyanobacteria aggregate in surface scums in tourist areas, and may kill domestic animals. Since the establishment of HAB related ICES activities (1984), we have witnessed the decline of some toxin-producers and PSP outbreaks in Iberia, the wax and wane of DSP outbreaks in Europe, and their emergence in North America, and the description of new lipophilic toxins (i.e. azaspiracids) that were unnoticed before, co-extracted with the most common diarrhetic shellfish toxins. Here we provide a checklist of the causative agents of harmful microalgal outbreaks in the ICES area, and a qualitative appraisal of the most outstanding patterns observed in the two last decades and reported to the ICES-IOC Working Group on Harmful Algal Bloom Dynamics.
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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 | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".