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Record W2508615376 · doi:10.1139/cjfas-2016-0035

Recruitment signals in juvenile cod surveys depend on thermal growth conditions

2016· article· en· W2508615376 on OpenAlexafffundvenueabout
Benjamin J. Laurel, David Côté, Robert S. Gregory, Lauren A. Rogers, Halvor Knutsen, Esben Moland Olsen

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsFisheries and Oceans CanadaOceans Limited (Canada)Amec Foster Wheeler (Canada)Memorial University of Newfoundland
FundersInterregFisheries and Oceans CanadaNorth Pacific Research BoardMemorial University of NewfoundlandNational Oceanic and Atmospheric AdministrationParks Canada
KeywordsGadusJuvenileAtlantic codFisheryAbundance (ecology)HabitatGadidaeClimate changeEnvironmental scienceEcologyOceanographyGeographyBiologyFish <Actinopterygii>Geology

Abstract

fetched live from OpenAlex

Coastal seine surveys contain some of the only direct measures of age-0 abundance for Atlantic cod (Gadus morhua) and Pacific cod (Gadus macrocephalus), yet their utility in forecasting future year-class strength has not been evaluated among regions. We analyzed coastal time series from the Gulf of Alaska, Newfoundland, and Norway to test the hypothesis that recruitment signals are stronger when assessed under thermal conditions that provide high juvenile growth potential. Weaker recruitment signals were associated with low growth potential from cold winters (Newfoundland) and recent warmer summers (Norway). We conclude that temperature-dependent growth strongly influences the utility of coastal surveys in recruitment forecasting among regions. Temporal changes in growth potential (e.g., via climate change) will likely affect recruitment signals by way of changes in juvenile mortality or spatial shifts to more favorable thermal habitats.

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)0.002
metaresearch head score (Gemma)0.008
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 score0.987
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.058
GPT teacher head0.272
Teacher spread0.215 · 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

Citations25
Published2016
Admission routes4
Has abstractyes

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