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Record W7096015862

Stock-specific ocean distribution and migration of chum salmon

2009· article· en· W7096015862 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicNuclear Structure and Function
Canadian institutionsnot available
Fundersnot available
KeywordsPacific oceanPelagic zoneOtolithHatcheryOncorhynchusStock (firearms)
DOInot available

Abstract

fetched live from OpenAlex

Abstract: Chum salmon (Oncorhynchus keta) is a major pelagic fish species in the Bering Sea and North Pacific ecosystems. The stock-specific ocean distribution of chum salmon was estimated by genetic stock identification (GSI) and hatchery otolith marks. Fish were caught by 1-h trawls at 98 stations in the Bering Sea, North Pacific Ocean and Gulf of Alaska during the early summer (June/July) and late summer/early fall (August/September) of 2003. Tissue samples were collected from chum salmon (n = 3,980) and run for 20 allozyme loci to estimate the stock composition of mixtures. In addition, otoliths were collected from chum salmon (n = 4,424) and examined for mark patterns to determine hatchery origin. The GSI-estimates combined with catch data (CPUE) indicated that the ocean distribution patterns of immature chum salmon were different among eleven regional stocks. Japanese stocks were mainly distributed in the Bering Sea during summer and early fall. The distribution of Russian (primarily northern Russian) stocks was similar to that of Japanese chum salmon, but they also spread into the North Pacific Ocean. Northwest Alaska stocks including fall runs in the Yukon River were relatively abundant at the southern edge (50°N) of the Gulf of Alaska and eastern North Pacific Ocean. Alaska Peninsula/Kodiak Island

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)0.000
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 score0.015
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.005
GPT teacher head0.206
Teacher spread0.201 · 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
Published2009
Admission routes1
Has abstractyes

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