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Record W2751694237 · doi:10.1139/cjfas-2017-0219

An ichthyophoniasis epizootic in Atlantic herring in marine waters around Iceland

2017· article· en· W2751694237 on OpenAlexvenueno aff
Guðmundur J. Óskarsson, Jónbjörn Pálsson, Asta Gudmundsdóttir

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsnot available
Fundersnot available
KeywordsEpizooticClupeaHerringFisheryStock (firearms)Atlantic herringBiologyClupeidaeMortality rateStock assessmentGeographyZoologyDemographyFishingOutbreakFish <Actinopterygii>

Abstract

fetched live from OpenAlex

A widespread ichthyophoniasis epizootic occurred in the Icelandic summer-spawning herring (Clupea harengus) stock during the years 2008–2014. The spatial and temporal variation in prevalence of heart lesions and the seasonal development of the disease in the stock were explored with an inspection of hearts. The year classes from 2004 to 2006 had generally the highest prevalence of heart lesions, varying from ∼47%–50% in 2009 to ∼31%–34% in 2014. Newly developed disease was apparently occurring in the autumns of 2008–2010 but not in the autumns thereafter. Analyses of the data strongly suggest that the disease caused mortality in 2009–2011 but was insignificant thereafter, and consequently, the epizootic waned after mid-2011. The analytical assessment model used for the stock gave the best fit to the data when applying disease mortality equivalent to 30% of the diseased herring for these 3 years. Thus, we conclude that the widespread ichthyophoniasis epizootic in the stock over these 6 years caused significant mortality in the stock during the first 3 years but less mortality than suggested in other publications.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.133
Threshold uncertainty score0.264

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.024
GPT teacher head0.235
Teacher spread0.211 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations9
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Fisheries and Aquatic Sciences→Same topicMarine animal studies overview→French-language works237,207→