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Record W4403446581 · doi:10.1126/sciadv.adn7118

Pathogens from salmon aquaculture in relation to conservation of wild Pacific salmon in Canada

2024· review· en· W4403446581 on OpenAlexafffundabout
Martin Krkošek, Andrew W. Bateman, Arthur L. Bass, William S. Bugg, Brendan Connors, Christoph Deeg, Emiliano Di Cicco, Sean C. Godwin, Jaime Grimm, Leila Krichel, Gideon Mordecai, Alexandra Morton, Stephanie J. Peacock, Dylan Shea, Brian Riddell, Kristina M. Miller

Bibliographic record

VenueScience Advances · 2024
Typereview
Languageen
FieldImmunology and Microbiology
TopicAquaculture disease management and microbiota
Canadian institutionsRaincoast Conservation FoundationUniversity of British ColumbiaFisheries and Oceans CanadaAsia Pacific Foundation of CanadaUniversity of TorontoVancouver Coastal Health
FundersFisheries and Oceans CanadaLiber Ero FoundationPacific Salmon FoundationMitacsGenome British Columbia
KeywordsAquacultureLepeophtheirusFisheryBiologyPredationFish farmingEcologyFish <Actinopterygii>

Abstract

fetched live from OpenAlex

The spread of pathogens from farmed salmon is a conservation concern for wild Pacific salmon in British Columbia (BC), Canada. Three pathogens are prevalent in farmed Atlantic salmon in BC, spill over to wild Pacific salmon, and are linked to negative impacts on wild salmon: Piscine orthoreovirus, Tenacibaculum spp., and sea lice ( Lepeophtheirus salmonis ). Molecular screening of infectious agents in farmed and wild salmon and environmental DNA highlights a further 4 agents that are likely elevated near salmon farms and 37 that co-occur in wild and farmed salmon. Pathogens likely affect wild salmon indirectly by mediating migration, competition, and predation. Current net-pen aquaculture practices pose these risks to numerous populations of all species of wild salmon in BC, most of which are not covered in Government of Canada science and advisory reports. Climate change, pathogen evolution, and changes to disease management and aquaculture regulations will influence future risks.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.983
Threshold uncertainty score0.867

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
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 head0.016
GPT teacher head0.275
Teacher spread0.259 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations17
Published2024
Admission routes3
Has abstractyes

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