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Record W2013206554 · doi:10.1577/h09-026.1

Susceptibility of Three Stocks of Pacific Herring to Viral Hemorrhagic Septicemia

2010· article· en· W2013206554 on OpenAlexfundno aff
Paul K. Hershberger, Jacob L. Gregg, Courtney Grady, Rachael Collins, James R. Winton

Bibliographic record

VenueJournal of Aquatic Animal Health · 2010
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAquaculture disease management and microbiota
Canadian institutionsnot available
FundersFisheries and Oceans CanadaMassachusetts Department of Fish and GameU.S. Geological SurveyExxon Valdez Oil Spill Trustee CouncilCalifornia Department of Fish and WildlifeU.S. Department of the Interior
KeywordsHerringBiologyZoologyFisheryPacific herringVirologyFish <Actinopterygii>

Abstract

fetched live from OpenAlex

Laboratory challenges using specific-pathogen-free Pacific herring Clupea pallasii from three distinct populations indicated that stock origin had no effect on susceptibility to viral hemorrhagic septicemia (VHS). All of the populations were highly susceptible to the disease upon initial exposure, with significantly greater cumulative mortalities occurring in the exposed treatment groups (56.3-64.3%) than in the unexposed control groups (0.8-9.0%). Interstock differences in cumulative mortality were not significant. The virus loads in the tissues of fish experiencing mortality were 10-10,000 times higher during the acute phase of the epizootics (day 13 postexposure) than during the recovery phase (days 30-42). Survivors of the epizootics were refractory to subsequent VHS, with reexposure of VHS survivors resulting in significantly less cumulative mortality (1.2-4.0%) than among positive controls (38.1-64.4%); interstock differences in susceptibility did not occur after reexposure. These results indicate that data from experiments designed to understand the ecology of VHS virus in a given stock of Pacific herring are broadly applicable to stocks throughout the northeastern Pacific.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.783
Threshold uncertainty score0.551

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.013
GPT teacher head0.276
Teacher spread0.263 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations32
Published2010
Admission routes1
Has abstractyes

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