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Record W4213087028 · doi:10.1139/cjfas-2020-0424

Temperature exposure in cod driven by changes in abundance

2022· article· en· W4213087028 on OpenAlexvenueno aff
Gotje von Leesen, Bjarte Bogstad, Einar Hjörleifsson, Ulysses S. Ninnemann, Steven E. Campana

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsnot available
FundersUniversitetet i Bergen
KeywordsGadusOtolithAbundance (ecology)Environmental scienceAtlantic codArcticFisheryPopulationPopulation densityOceanographyEcologyBiologyFish <Actinopterygii>Geology

Abstract

fetched live from OpenAlex

Animals actively select the most suitable habitat in terms of fitness, much of which is mediated by temperature. We reconstructed population abundance, oxygen isotope and temperature chronologies for the Icelandic and the Northeast Arctic (NEA) cod (Gadus morhua) populations to determine if their temperature selectivity over the last 100 years was driven by rising water temperatures and (or) changes in abundance. Individual annual growth increments from immature and mature life history stages of cod collected in southern Iceland and the Lofoten area (Norway) were micromilled from adult otoliths and then assayed for stable oxygen isotopes (δ18Ootolith). Linear mixed effect models were used to identify and quantify the density-dependent temperature exposure of both cod populations. The results indicated that Icelandic cod migrated into warmer waters with increasing abundance (p < 0.05), whereas NEA cod moved into colder waters (p < 0.001). Our results suggest that thermal preferences and density-dependent effects can be used to forecast potential redistribution scenarios of fish as oceans warm.

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 score0.015
Threshold uncertainty score0.029

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)0.0010.000

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 head0.013
GPT teacher head0.216
Teacher spread0.202 · 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

Citations6
Published2022
Admission routes1
Has abstractyes

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