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Record W4214581570 · doi:10.1093/icesjms/fsac036

The need to see a bigger picture to understand the ups and downs of Pacific salmon abundances

2022· article· en· W4214581570 on OpenAlexaffabout
Richard J. Beamish

Bibliographic record

VenueICES Journal of Marine Science · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsFisheries and Oceans Canada
Fundersnot available
KeywordsAbundance (ecology)Pacific oceanJuvenileClimate changeOceanographyFisheryEcosystemMarine ecosystemEnvironmental scienceScale (ratio)GeographyEcologyBiologyGeology

Abstract

fetched live from OpenAlex

Abstract There are more Pacific salmon in the ocean recently than in recorded history. Increases are believed to be related to shifts in climate but specific, biologically based mechanisms linking climate to increases are not known. At the same time, Pacific salmon abundances in Japan and on Canada's west coast are at historic low levels with attempts to stop the decline unsuccessful. Most juvenile salmon that enter the ocean die, resulting in large abundance increases and decreases from small changes in the already very low ocean survival. Because of this sensitivity to changes in ocean ecosystems and because of the recent basin-scale fluctuations in trends in abundance, I propose that it is time to see a bigger picture and improve the understanding of the biological mechanisms that most influence ocean survival. I leave it to readers to decide if my example of Pacific salmon is part of a more general need in fisheries science to better understand the biological mechanisms linking survival to climate.

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)0.003
metaresearch head score (Gemma)0.006
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.005
Scholarly communication0.0040.018
Open science0.0010.002
Research integrity0.0040.009
Insufficient payload (model declined to judge)0.0180.002

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 head0.012
GPT teacher head0.249
Teacher spread0.237 · 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

Citations20
Published2022
Admission routes2
Has abstractyes

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