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Record W7133284833

Chinook in Southern Resident Killer Whale Critical Habitat

2025· other· en· W7133284833 on OpenAlexfundaboutno aff
Fisheries and Oceans Canada, Pêches et Océans Canada

Bibliographic record

VenueFederal Open Science Repository of Canada / Le Dépôt fédéral de science ouverte du Canada · 2025
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
FundersFisheries and Oceans Canada
KeywordsChinook windOncorhynchusPredationFish measurementCritical habitatHabitatStock (firearms)Stock assessment
DOInot available

Abstract

fetched live from OpenAlex

Chinook salmon are a key prey of Southern Resident Killer Whales (SRKW) and variability in individual traits (e.g., body size, lipid content) affects their nutritional value to SRKW. Spatiotemporal distribution, local abundance, and fish behavior may influence the accessibility and availability of Chinook salmon to SRKW. Using recreational fisheries data (2014-2023), a geostatistical model was developed to estimate the stock composition and the size composition of Chinook salmon within SRKW critical habitat in Canadian waters, during May-September, when SRKW diets are dominated by Chinook salmon. To investigate SRKW prey selectivity, a co-occurrence approach was taken to compare the size and stock composition of SRKW prey remains in relation to model estimates from recreational fishery samples, in the western portion of SRKW critical habitat from June to September (2017-2023). All Chinook salmon stocks observed in the recreational fisheries data occurred in SRKW prey remains. Fraser River Spring 52, Summer 52, and Summer 41 stocks were more common, while Puget Sound, West Coast Vancouver Island (WCVI), Columbia River Summer/Fall, and “Other” stocks were less common, in prey remains than predicted by the recreational fishery model. Chinook salmon estimated to be smaller than 75 cm fork length were rarely observed in SRKW prey remains. Chinook salmon greater than 75 cm fork length were more common, while fish between 55 and 75 cm fork length were less common than predicted by the recreational fishery model. While total terminal abundance summed across all stocks considered here has remained relatively stable since 1982, Fraser River Spring 52 and Summer 52 Chinook salmon populations have declined in abundance, while the Fraser River Summer 41 and WCVI stocks have increased in abundance. Puget Sound and Columbia River Summer/Fall abundance has varied cyclically. SRKW have poor body condition in the spring. In the current study, SRKW showed selectivity towards Chinook salmon stocks with high lipid content and Chinook salmon individuals with large body size. Therefore, improvements to the SRKW prey field in Canadian critical habitat may occur via increasing the abundance of Chinook salmon with early migration timing, high lipid content, and large body size. Independent of abundance, SRKW may also benefit from increases in prey quality as represented by the body size and/or lipid content of Chinook salmon stocks. Key sources of uncertainty in the SRKW prey remains data include potential sampling bias, as well as low precision due to a relatively small sample size. A key source of uncertainty associated with characterizing Chinook salmon available to SRKWs is the extent to which fisheries-dependent samples accurately represent the underlying prey base. The importance of Chinook salmon stocks outside of Canadian critical habitat and during other periods of the year was not considered here because prey remains samples were not available. Non Chinook salmon and other species present in SRKW diets were not evaluated and warrant additional research.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.699
Threshold uncertainty score0.598

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.000

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 head0.008
GPT teacher head0.248
Teacher spread0.240 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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