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Record W3132712574 · doi:10.1371/journal.pone.0247370

Can late stage marine mortality explain observed shifts in age structure of Chinook salmon?

2021· article· en· W3132712574 on OpenAlexaboutno aff
Kaitlyn Manishin, Curry J. Cunningham, Peter A. H. Westley, Andrew C. Seitz

Bibliographic record

VenuePLoS ONE · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsnot available
Fundersnot available
KeywordsChinook windStage (stratigraphy)BiologyFisheryDemographyOncorhynchusFish <Actinopterygii>

Abstract

fetched live from OpenAlex

Chinook salmon (Oncorhynchus tshawytscha) populations have experienced widespread declines in abundance and abrupt shifts toward younger and smaller adults returning to spawn in rivers. The causal agents underpinning these shifts are largely unknown. Here we investigate the potential role of late-stage marine mortality, defined as occurring after the first winter at sea, in driving this species' changing age structure. Simulations using a stage-based life cycle model that included additional mortality during after the first winter at sea better reflected observed changes in the age structure of a well-studied and representative population of Chinook salmon from the Yukon River drainage, compared with a model estimating environmentally-driven variation in age-specific survival alone. Although the specific agents of late-stage mortality are not known, our finding is consistent with work reporting predation by salmon sharks (Lamna ditropis) and marine mammals including killer whales (Orcinus orca). Taken as a whole, this work suggests that Pacific salmon mortality after the first winter at sea is likely to be higher than previously thought and highlights the need to investigate selective sources of mortality, such as predation, as major contributors to rapidly changing age structure of spawning adult Chinook 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 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)0.001
metaresearch head score (Gemma)0.003
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 score0.040
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.065
GPT teacher head0.247
Teacher spread0.182 · 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

Citations21
Published2021
Admission routes1
Has abstractyes

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