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Record W3005975651 · doi:10.1101/2020.02.13.948026

Emerging viruses in British Columbia salmon discovered via a viral immune response biomarker panel and metatranscriptomic sequencing

2020· preprint· en· W3005975651 on OpenAlexafffundabout
Gideon Mordecai, Emiliano Di Cicco, Oliver P. Günther, Angela D. Schulze, Karia H. Kaukinen, Shaorong Li, Amy Tabata, Tobi J. Ming, Hugh Ferguson, Curtis A. Suttle, Kristina M. Miller

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2020
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Virus Infections Studies
Canadian institutionsFisheries and Oceans CanadaUniversity of British Columbia
FundersHakai Institute
KeywordsChinook windBiologySalmoOncorhynchusAquacultureFisheryVirusZoologySalmonidaeVirologyFish <Actinopterygii>

Abstract

fetched live from OpenAlex

Abstract The emergence of infectious agents poses a continual economic and environmental challenge to aquaculture production, yet the diversity, abundance and epidemiology of aquatic viruses are poorly characterised. In this study, we applied salmon host transcriptional biomarkers to identify and select fish in a viral disease state but only those that we also showed to be negative for established viruses. This was followed by metatranscriptomic sequencing to determine the viromes of dead and dying farmed Atlantic ( Salmo salar ) and Chinook ( Oncorhynchus tshawytscha ) salmon in British Columbia. We found that the application of the biomarker panel increased the probability of discovering viruses in aquaculture populations. We discovered viruses that have not previously been characterized in British Columbian Atlantic salmon farms. To determine the epidemiology of the newly discovered or emerging viruses we conducted high-throughput RT-PCR to reveal their prevalence in British Columbia (BC), and detected some of the viruses we first discovered in farmed Atlantic salmon in Chinook and sockeye salmon, suggesting a broad host range. Finally, we applied in-situ hybridisation to confirm infection and explore the tissue tropism of each virus.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.932
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.036
GPT teacher head0.224
Teacher spread0.189 · 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

Citations10
Published2020
Admission routes3
Has abstractyes

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