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Record W4321606365 · doi:10.1139/cjfas-2022-0232

Quantifying genetic differentiation and population assignment between two contingents of Atlantic mackerel (<i>Scomber scombrus</i>) in the Northwest Atlantic

2023· article· en· W4321606365 on OpenAlexaffvenueabout
Audrey Bourret, Andrew Smith, Elisabeth Van Beveren, Stéphane Plourde, Kiersten L. Curti, Teunis Jansen, David E. Richardson, Martín Castonguay, Naiara Rodríguez‐Ezpeleta, Geneviève J. Parent

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicIdentification and Quantification in Food
Canadian institutionsFisheries and Oceans Canada
Fundersnot available
KeywordsScomberMackerelFisheryScombridaePopulationBiologyGeographyFish <Actinopterygii>DemographyTuna

Abstract

fetched live from OpenAlex

In the Northwest Atlantic (NWA), the Atlantic mackerel ( Scomber scombrus) has a northern and a southern contingent, which spawn in Canada and the United States (U.S.), respectively. Both contingents mix mostly along the U.S. shelf during overwintering. The discrimination of individuals from each contingent in fisheries could improve the management of this depleted species in both countries. Here, we used genome-wide markers (&gt;10 000 single nucleotide polymorphisms (SNPs)) to assess genomic differences between mackerel of both contingents, and possibly infer the proportions of each contingent in NWA management units. Small but significant genetic differentiation was observed between the northern and southern contingents ( F ST = 0.0010). Genetic assignments to reference samples from the two contingents were performed with predictive accuracy &gt; 85%. Fish from both contingents were present along the U.S. shelf during late winter and early spring but also, without prior evidence of this, likely in Canadian waters from late spring to fall. Genetic assignments could be used as a stock discrimination tool so that fishery removals can be effectively determined and managed on a contingent level.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.046
GPT teacher head0.281
Teacher spread0.235 · 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

Citations6
Published2023
Admission routes3
Has abstractyes

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