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Record W4315880899 · doi:10.1111/fog.12628

The influence of temperature on Pacific hake co‐occurrence with euphausiids in the <scp>California Current Ecosystem</scp>

2023· article· en· W4315880899 on OpenAlexaffabout
Elizabeth M. Phillips, Michael J. Malick, Stéphane Gauthier, Melissa A. Haltuch, Mary E. Hunsicker, Sandra L. Parker‐Stetter, Rebecca Thomas

Bibliographic record

VenueFisheries Oceanography · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsUniversity of VictoriaFisheries and Oceans Canada
FundersNational Oceanic and Atmospheric AdministrationNational Research Council
KeywordsKrillHakeOceanographyFisheryAntarctic krillMerlucciusEnvironmental scienceMarine ecosystemSea surface temperatureEcosystemBiologyEcologyGeologyFish <Actinopterygii>

Abstract

fetched live from OpenAlex

Abstract Understanding the influence of ocean conditions on predator–prey relationships can provide insight for ecosystem‐based fisheries management. Pacific hake ( Merluccius productus ) are abundant and commercially important groundfish in the California Current Ecosystem (CCE) that consume euphausiids (krill) as a major prey item. We used data from the biennial joint U.S.‐Canada Integrated Ecosystem &amp; Acoustic Trawl Survey for Pacific hake (2007–2019, n = 8 surveys) to quantify co‐occurrence of age 2+ hake with krill in relation to bottom depth, continental shelf break location, surface chlorophyll‐a, and 100‐m temperature. Vertical distributions of hake varied among years and were not correlated to krill depth. Hake hotspots occurred primarily off the Oregon coast and near Cape Mendocino, while most krill hotspots occurred in the northern CCE. The probability of co‐occurrence was greatest during cool ocean conditions (100‐m temperature 1°C below average), averaging 41.0% and extending throughout most of the CCE. During warm ocean conditions (100‐m temperature 1°C above average), predicted co‐occurrence averaged 17.0% and was concentrated near Cape Mendocino. These results indicate that hake‐krill co‐occurrence is a function of predator and prey spatial distributions and overall krill abundance. Furthermore, temperature influences hake‐krill co‐occurrence and may explain some of the variation in hake growth and recruitment to the fishery.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.530
Threshold uncertainty score0.571

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.011
GPT teacher head0.234
Teacher spread0.222 · 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.

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

Citations7
Published2023
Admission routes2
Has abstractyes

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