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Record W2786618857 · doi:10.1139/cjfas-2017-0254

Genomic tools for management and conservation of Atlantic cod in a coastal marine protected area

2018· article· en· W2786618857 on OpenAlexafffundvenueabout
Marion Sinclair‐Waters, Paul Bentzen, Corey J. Morris, Daniel E. Ruzzante, Matthew Kent, Sigbjørn Lien, Ian Bradbury

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicIdentification and Quantification in Food
Canadian institutionsFisheries and Oceans CanadaDalhousie University
FundersFisheries and Oceans Canada
KeywordsBayGadusAtlantic codMarine protected areaFisheryPopulationOceanographyGeographyBiologyEnvironmental scienceEcologyGeologyFish <Actinopterygii>

Abstract

fetched live from OpenAlex

Marine protected areas (MPAs) can serve as effective tools for the management and conservation of exploited marine species. The Gilbert Bay MPA in coastal Labrador was created to protect a genetically distinct population of Atlantic cod (Gadus morhua); however, decreases in abundance continue to occur potentially due to exploitation outside the MPA. We developed a single-nucleotide polymorphism (SNP) panel to identify Gilbert Bay cod in areas outside MPA boundaries where mixing with offshore cod occurs. In total, 361 individuals from Gilbert Bay, surrounding areas, and offshore were genotyped for 10 913 SNPs. Using F ST rankings and guided regularized random forest, we selected 23 SNPs that together generate 100% accuracy in individual assignment and accurately estimate the proportion of Gilbert Bay cod in fishery samples from sites outside MPA boundaries: on average, fishery samples included 17.3% Gilbert Bay cod. Estimates of effective population size for the Gilbert Bay population ranged from 655 to 1114. Our findings demonstrate the power of using genomic approaches for management of an exploited marine species and enhancing the design of MPAs.

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 score0.121
Threshold uncertainty score1.000

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 head0.036
GPT teacher head0.244
Teacher spread0.208 · 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

Citations18
Published2018
Admission routes4
Has abstractyes

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