MétaCan
Menu
Back to cohort
Record W2966672506 · doi:10.1002/fee.2090

Determining natal origin for improved management of Atlantic bluefin tuna

2019· review· en· W2966672506 on OpenAlexaff
Naiara Rodríguez‐Ezpeleta, Natalia Díaz‐Arce, John F. Walter, David E. Richardson, Jay R. Rooker, Leif Nøttestad, Alex Hanke, James S. Franks, Simeon Deguara, Matthew V. Lauretta, Piero Addis, José Luis Varela, Igaratza Fraile, Nicolás Goñi, Noureddine Abid, Francisco Alemany, I. K. Oray, Joseph M. Quattro, Fambaye Ngom Sow, T. Itoh, F. Saadet Karakulak, P.J. Pascual-Alayón, Miguel N. Santos, Yohei Tsukahara, Molly E. Lutcavage, Jean‐Marc Fromentin, Haritz Arrizabalaga

Bibliographic record

VenueFrontiers in Ecology and the Environment · 2019
Typereview
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsAcadia UniversityFisheries and Oceans Canada
FundersEuropean Social FundDepartment of Agriculture and Fisheries, Queensland GovernmentEusko JaurlaritzaEuropean Regional Development FundUniversity of Southern MississippiMinisterio de Economía y CompetitividadErzincan Üniversitesi
KeywordsTunaFisheryThunnusFisheries managementPopulationStock (firearms)BiologyContext (archaeology)Mediterranean seaStock assessmentGeographyMediterranean climateFish <Actinopterygii>EcologyFishing

Abstract

fetched live from OpenAlex

Effective sustainable management of marine fisheries requires that assessed management units (that is, fish stocks) correspond to biological populations. This issue has long been discussed in the context of Atlantic bluefin tuna ( ABFT , Thunnus thynnus ) management, which currently considers two unmixed stocks but does not take into account how individuals born in each of the two main spawning grounds (Gulf of Mexico and Mediterranean Sea) mix in feeding aggregations throughout the Atlantic Ocean. Using thousands of genome‐wide molecular markers obtained from larvae and young of the year collected at the species’ main spawning grounds, we provide what is, to the best of our knowledge, the first direct genetic evidence for “natal homing” in ABFT . This has facilitated the development of an accurate, cost‐effective, and non‐invasive tool for tracing the genetic origin of ABFT that allows for the assignment of catches to their population of origin, which is crucial for ensuring that ABFT management is based on biologically meaningful stock units rather than simply on catch location.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.960
Threshold uncertainty score0.952

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.240
Teacher spread0.225 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations89
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueFrontiers in Ecology and the EnvironmentSame topicFish Ecology and Management StudiesFrench-language works237,207