MétaCan
Menu
Back to cohort
Record W4408170535 · doi:10.1080/23308249.2025.2471085

Spatial and Temporal Size Distribution of Swordfish ( <i>Xiphias gladius</i> ) in the Atlantic Ocean: Implications for Conservation and Management

2025· article· en· W4408170535 on OpenAlexaff
Daniela Rosa, Michael J. Schirripa, Kyle Gillespie, David Macías, Rodrigo Forselledo, Bruno Leite Mourato, Mikihiko Kai, Freddy Arocha, Nan‐Jay Su, Sven Kerwath, Laurent Bahou, Luigi Pappalardo, Guillermo A. Diaz, Pedro G. Lino, Francisca Salmerón, Josetxu Ortiz de Urbina, Luis Gustavo Cardoso, Rodrigo Sant’Ana, Paulo Travassos, Karim Erzini, Miguel N. Santos, Andrés Domingo, José Carlos Báez, Alex Hanke, Craig M. Brown, Rui Coelho

Bibliographic record

VenueReviews in Fisheries Science & Aquaculture · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsFisheries and Oceans Canada
FundersEuropean Social FundFundação para a Ciência e a Tecnologia
KeywordsSwordfishFisheryEnvironmental scienceGeographyBiologyTunaFish <Actinopterygii>

Abstract

fetched live from OpenAlex

Swordfish (Xiphias gladius) is a common target species of surface pelagic longline fisheries. In the Atlantic Ocean and Mediterranean Sea, swordfish is managed as three separate stocks, all having management measures in place to rebuild or conserve the stocks, including minimum landing sizes. The objective of this study was to review size data for swordfish in the Atlantic Ocean, model the sex-specific size distribution and determine areas where there is higher likelihood of capturing undersized fish. The size distribution differed between males and females and varied by quarter, indicating movements of large fish between temperate and tropical waters. Undersized fish seems to occur in association with coastal waters, with higher proportions in the Northwest Atlantic and tropical areas. This study provides a better understanding of the temporal and spatial size and sex distribution of swordfish and presents insights into the distribution of undersized swordfish that is subject to management measures.

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 score0.333
Threshold uncertainty score0.308

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.025
GPT teacher head0.287
Teacher spread0.261 · 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

Citations5
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueReviews in Fisheries Science & AquacultureSame topicMarine and fisheries researchFrench-language works237,207