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Record W1515965890 · doi:10.17895/ices.pub.24752484

Distribution and impacts of Harmful Algal Blooms in the ICES area

2014· article· en· W1515965890 on OpenAlexaboutno aff
Beatriz Reguera, Eileen Bresnan

Bibliographic record

VenueOpen MIND · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine Toxins and Detection Methods
Canadian institutionsnot available
Fundersnot available
KeywordsAlgal bloomOutbreakFish killFisheryMarine toxinDinoflagellateGeographyFishingBenthic zoneAquacultureEcologyEnvironmental scienceBiologyPhytoplanktonFish <Actinopterygii>ToxinNutrient

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.772
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.025
GPT teacher head0.303
Teacher spread0.278 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2014
Admission routes1
Has abstractyes

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Same venueOpen MINDSame topicMarine Toxins and Detection MethodsFrench-language works237,207