MétaCan
Menu
Back to cohort
Record W4406126396 · doi:10.1080/23308249.2024.2446806

Gap Analysis on the Biology of Marine Fishes Across European Seas

2025· article· en· W4406126396 on OpenAlexaff
Kathleen Kesner‐Reyes, Emily C. Capuli, Rodolfo B. Reyes, Jeremiah Glenn M. Jansalin, Josephine Rius-Barile, Manuel Bactong, Eva Daskalaki, Sophie Manousi, Carmen Ferrà, Gianpaolo Coro, Francesc Ordines, Liesa Celie, Marco Scotti, Charlotte Lambert, Gideon Gal, Maria Lourdes D. Palomares, Athanassios C. Tsikliras, Donna Dimarchopoulou

Bibliographic record

VenueReviews in Fisheries Science & Aquaculture · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsUniversity of British ColumbiaFisheries and Oceans Canada
FundersHorizon Therapeutics
KeywordsFisheryOceanographyMarine biologyBiologyEcologyGeology

Abstract

fetched live from OpenAlex

This review evaluates the current knowledge of essential biological traits (diet, fecundity, maturity, length-weight relationships, spawning, growth, lifespan, and natural mortality) of marine fishes across European and adjacent waters. These traits are crucial for ecosystem modeling and stock assessments. Using data from FishBase, the largest and most comprehensive database on fishes, a gap analysis was performed to identify areas of research focus and the corresponding gaps that require further study. Biological data coverage is strong in the Baltic and North Seas but moderate in the Adriatic, Aegean, Biscay, Celtic, Levantine, and western Mediterranean Seas. Well-documented species include the European conger (Conger conger), thornback ray (Raja clavata), and transparent goby (Aphia minuta) which are reported from all areas. The narrowest knowledge gaps concern length-weight relationships, followed by spawning and growth, while natural mortality and fecundity are the least studied biological characteristics. Regional variations exist, particularly for protected species. Future research should focus on filling gaps by addressing overlooked species (bycatch and discarded species) and traits such as natural mortality and fecundity, with special attention to vulnerable groups like sharks and rays. Expanding biological data coverage will reduce uncertainties in stock assessments and improve ecosystem models, two widely used tools for sustainable fisheries management and marine conservation.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.819
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.006
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.047
GPT teacher head0.326
Teacher spread0.279 · 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.

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

Citations3
Published2025
Admission routes1
Has abstractyes

Explore more

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