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Record W4213455486 · doi:10.1139/facets-2021-0052

Way out there: pathogens, health, and condition of overwintering salmon in the Gulf of Alaska

2022· article· en· W4213455486 on OpenAlexaffvenue
Christoph Deeg, А. Н. Канзепарова, Alexei Somov, Svetlana Esenkulova, Emiliano Di Cicco, Karia H. Kaukinen, Amy Tabata, Tobi J. Ming, Shaorong Li, Gideon Mordecai, Angela D. Schulze, Kristina M. Miller

Bibliographic record

VenueFACETS · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsFisheries and Oceans CanadaPacific Salmon FoundationUniversity of British Columbia
Fundersnot available
KeywordsOverwinteringBiologyFisheryEcosystemPredationOncorhynchusEcologyStock (firearms)GeographyFish <Actinopterygii>

Abstract

fetched live from OpenAlex

Salmon are keystone species across the North Pacific, supporting ecosystems, commercial opportunities, and cultural identity. Nevertheless, many wild salmon stocks have experienced significant declines. Salmon restoration efforts focus on fresh and coastal waters, but little is known about the open ocean environment. Here we use high throughput RT-qPCR tools to provide the first report on the health, condition, and infection profile of coho, chum, pink, and sockeye salmon in the Gulf of Alaska during the 2019 winter. We found lower infectious agent number, diversity, and burden compared with coastal British Columbia in all species except coho, which exhibited elevated stock-specific infection profiles. We identified Loma sp. and Ichthyophonus hoferi as key pathogens, suggesting transmission in the open ocean. Reduced prey availability, potentially linked to change in ocean conditions due to an El Niño event, correlated with energetic deficits and immunosuppression in salmon. Immunosuppressed individuals showed higher relative infection burden and higher prevalence of opportunistic pathogens. We highlight the cumulative effects of infection and environmental stressors on overwintering salmon, establishing a baseline to document the impacts of a changing ocean on salmon.

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 score0.059
Threshold uncertainty score0.505

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 head0.014
GPT teacher head0.241
Teacher spread0.228 · 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

Citations19
Published2022
Admission routes2
Has abstractyes

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