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Record W1973656645 · doi:10.7589/0090-3558-46.4.1242

MASS MORTALITY ASSOCIATED WITH KOI HERPESVIRUS IN WILD COMMON CARP IN CANADA

2010· article· en· W1973656645 on OpenAlexafffundabout
Kyle A. Garver, Lowia Al-Hussinee, Laura M. Hawley, Tamara Schroeder, Sandra Edes, Véronique LePage, Elena Contador, Spencer Russell, Roselynn M. W. Stevenson, Brian W. Souter, Elizabeth Wright, John S. Lumsden

Bibliographic record

VenueJournal of Wildlife Diseases · 2010
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAquaculture disease management and microbiota
Canadian institutionsMinistry of Natural Resources and ForestryUniversity of GuelphFisheries and Oceans Canada
FundersMinistry of Natural Resources
KeywordsBiologyCarpPolymerase chain reactionVeterinary medicineCommon carpVirologyInterstitial nephritisCyprinusFish <Actinopterygii>FisheryGeneGeneticsKidneyMedicine

Abstract

fetched live from OpenAlex

Koi herpesvirus (KHV) was identified as being associated with more than one mortality event affecting common carp in Canada. The first was an extensive mortality event that occurred in 2007 in the Kawartha Lakes region, Ontario, affecting Lakes Scugog and Pigeon. Fish had branchial necrosis and hepatic vasculitis with an equivocal interstitial nephritis. Several fish also had branchial columnaris. Subsequent mortality events occurred in 2008 in additional bodies of water in south-central Ontario, such as Lake Katchewanooka and outside of Ontario in Lake Manitoba, Manitoba. Koi herpesvirus was detected in fish submitted for examination from all of these lakes by polymerase chain reaction (PCR), and sequence of the PCR product revealed 100% homology to KHV strains U and I. Real-time PCR analysis of KHV-infected wild carp revealed viral loads ranging from 6.02×10(1) to 2.4×10(6) copies μg(-1) host DNA. This is the first report of KHV in Canada.

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)0.001
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 score0.091
Threshold uncertainty score0.183

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.216
Teacher spread0.210 · 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

Citations63
Published2010
Admission routes3
Has abstractyes

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