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Record W2811248695 · doi:10.1111/jfd.12832

In vivo virulence and genomic comparison of infectious Salmon Anaemia Virus isolates from Atlantic Canada

2018· article· en· W2811248695 on OpenAlexaffabout
Francis LeBlanc, Steven Leadbeater, Mark Laflamme, Nellie Gagné

Bibliographic record

VenueJournal of Fish Diseases · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Virus Infections Studies
Canadian institutionsFisheries and Oceans Canada
Fundersnot available
KeywordsVirulenceBiologyVirologyVirusGenomeGeneMicrobiologyGenetics

Abstract

fetched live from OpenAlex

The infectious salmon anaemia virus (ISAV) is capable of causing a significant disease in Atlantic salmon, which has resulted in considerable financial losses for salmon farmers around the world. Since the first detection of ISAV in Canada in 1996, it has been a high priority for aquatic animal health management and surveillance programmes have led to the identification of many genetically distinct ISAV isolates of variable virulence. In this study, we evaluated the virulence of three ISAV isolates detected in Atlantic Canada in 2012 by doing in vivo-controlled disease challenges with two sources of Atlantic salmon. We measured viral loads in fish tissues during the course of infection. Sequences of the full viral RNA genomes of these three ISAV isolates were obtained and compared to a high-virulence and previously characterized isolate detected in the Bay of Fundy in 2004, as well as a newly identified ISAV NA-HPR0 isolate. All three ISAV isolates studied were shown to be of low to mid-virulence with fish from source A having a lower mortality rate than fish from source B. Viral load estimation using an RT-qPCR assay targeting viral segment 8 showed a high degree of similarity between tissues. Through genomic comparison, we identified various amino acid substitutions unique to some isolates, including a stop codon in the segment 8 ORF2 not previously reported in ISAV, present in the isolate with the lowest observed virulence.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.445
Threshold uncertainty score0.645

Codex and Gemma teacher scores by category

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 head0.013
GPT teacher head0.236
Teacher spread0.223 · 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 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

Citations6
Published2018
Admission routes2
Has abstractyes

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