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Record W4205475181 · doi:10.1111/fme.12531

Conflicting interests and growing importance of non‐indigenous species in commercial and recreational fisheries of the Mediterranean Sea

2022· article· en· W4205475181 on OpenAlexaboutno aff
Periklis Kleitou, Dimitrios K. Moutopoulos, Ioannis Giovos, Demetris Kletou, Ioannis Savva, Leda L. Cai, Jason M. Hall‐Spencer, Anastasia Charitou, María Elia, George Katselis, Siân E. Rees

Bibliographic record

VenueFisheries Management and Ecology · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine Ecology and Invasive Species
Canadian institutionsnot available
FundersMAVA Foundation
KeywordsFishingIndigenousFisherySustainabilityGeographyMediterranean climateRecreationRecreational fishingQuarter (Canadian coin)Mediterranean seaCommercial fishingBusinessEcologyBiology

Abstract

fetched live from OpenAlex

Abstract Non‐indigenous species (NIS) are spreading and reshaping Mediterranean Sea biological communities and fishery resources. The present study used fisheries data and structured interviews to assess the impacts of NIS on recreational and commercial fishers in Cyprus. NIS that have been present in Cyprus for more than two decades were mostly perceived by local fishers as native, NIS with high market value were considered to be beneficial, and venomous or poisonous NIS were considered to be deleterious. Pufferfishes (Tetraodontidae) were identified by fishers as causing significant economic damage, which undermines the sustainability of the commercial fishing sector. The most popular and highly priced NIS were rabbitfishes ( Siganus spp.). In terms of commercial landings, six non‐indigenous taxa contributed over a quarter of the total landing value and more than half during the summer season. The results of the present study emphasised the multifaceted interactions of NIS with the fishing sector, and how policy objectives may not align with social and commercial fishery interests.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.998

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.016
GPT teacher head0.222
Teacher spread0.207 · 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

Citations34
Published2022
Admission routes1
Has abstractyes

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