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Record W2510436833 · doi:10.1139/cjfas-2015-0430

Genetic population structure in Greenland halibut (<i>Reinhardtius hippoglossoides</i>) and its relevance to fishery management

2016· article· en· W2510436833 on OpenAlexvenueno aff
Jon‐Ivar Westgaard, Atal Saha, Matthew Kent, Hanne Hansen, Halvor Knutsen, Lorenz Hauser, Steven X. Cadrin, Ole Thomas Albert, Torild Johansen

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicIdentification and Quantification in Food
Canadian institutionsnot available
Fundersnot available
KeywordsHalibutFisheryPopulationBiologyPopulation structureGeographyFisheries managementHippoglossus hippoglossusGenetic structureEcologyFishingGenetic variationDemographyFish <Actinopterygii>

Abstract

fetched live from OpenAlex

Exploited marine resources can be managed more effectively when accurate information on geographic population structure is available. Genetic markers offer a powerful tool for fisheries management, because they reveal biologically sound management units. Single nucleotide polymorphisms (SNPs) markers derived from restriction-site associated DNA sequencing (RAD-seq) were developed and used to investigate the stock structure of Greenland halibut (Reinhardtius hippoglossoides). A total of 96 SNPs were analyzed from 384 individuals and eight locations across the Atlantic. Our results suggest a subdivision of Greenland halibut into two populations, an eastern Atlantic population and a western Atlantic population, with a proposed border across the Denmark Strait. In general, Greenland halibut display weak but significant population structure (overall F ST = 0.003; p &lt; 0.001), which can be explained by connectivity among populations owing to the migratory behavior or egg and larval drift.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.142
Threshold uncertainty score0.564

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.015
GPT teacher head0.231
Teacher spread0.216 · 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

Citations25
Published2016
Admission routes1
Has abstractyes

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