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Record W2126435936 · doi:10.1093/icesjms/fsq199

Pacific salmon abundance trends and climate change

2011· article· en· W2126435936 on OpenAlexaff
James R. Irvine, Masa‐aki Fukuwaka

Bibliographic record

VenueICES Journal of Marine Science · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsFisheries and Oceans Canada
Fundersnot available
KeywordsChinook windAbundance (ecology)OncorhynchusFisheryHatcheryMarine ecosystemClimate changeOceanographyGeographyPacific decadal oscillationRegime shiftPacific oceanEcosystemEnvironmental scienceEcologyBiologyFish <Actinopterygii>Geology

Abstract

fetched live from OpenAlex

Abstract Irvine, J. R., and Fukuwaka, M. 2011. Pacific salmon abundance trends and climate change. – ICES Journal of Marine Science, 68: 1122–1130. Understanding reasons for historical patterns in salmon abundance could help anticipate future climate-related changes. Recent salmon abundance in the northern North Pacific Ocean, as indexed by commercial catches, has been among the highest on record, with no indication of decline; the 2009 catch was the highest to date. Although the North Pacific Ocean continues to produce large quantities of Pacific salmon, temporal abundance patterns vary among species and areas. Currently, pink and chum salmon are very abundant overall and Chinook and coho salmon are less abundant than they were previously, whereas sockeye salmon abundance varies among areas. Analyses confirm climate-related shifts in abundance, associated with reported ecosystem regime shifts in approximately 1947, 1977, and 1989. We found little evidence to support a major shift after 1989. From 1990, generally favourable climate-related marine conditions in the western North Pacific Ocean, as well as expanding hatchery operations and improving hatchery technologies, are increasing abundances of chum and pink salmon. In the eastern North Pacific Ocean, climate-related changes are apparently playing a role in increasing chum and pink salmon abundances and declining numbers of coho and 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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.559
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.038
GPT teacher head0.271
Teacher spread0.233 · 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.

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

Citations108
Published2011
Admission routes1
Has abstractyes

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