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Record W3008892663 · doi:10.1126/sciadv.aaz3801

Illicit trade in marine fish catch and its effects on ecosystems and people worldwide

2020· article· en· W3008892663 on OpenAlex
U. Rashid Sumaila, Dirk Zeller, Lincoln Hood, Maria Lourdes D. Palomares, Y. Li, Daniel Pauly

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueScience Advances · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsFisheries and Oceans Canada
FundersSocial Sciences and Humanities Research Council of CanadaOak Foundation
KeywordsMarine ecosystemFish <Actinopterygii>EcosystemFisheryMarine fishBusinessEnvironmental scienceEcologyBiology

Abstract

fetched live from OpenAlex

Illegal, unreported, and unregulated fishing is widespread; it is therefore likely that illicit trade in marine fish catch is also common worldwide. We combine ecological-economic databases to estimate the magnitude of illicit trade in marine fish catch and its impacts on people. Globally, between 8 and 14 million metric tons of unreported catches are potentially traded illicitly yearly, suggesting gross revenues of US$9 to US$17 billion associated with these catches. Estimated loss in annual economic impact due to the diversion of fish from the legitimate trade system is US$26 to US$50 billion, while losses to countries' tax revenues are between US$2 and US$4 billion. Country-by-country estimates of these losses are provided in the Supplementary Materials. We find substantial likely economic effects of illicit trade in marine fish catch, suggesting that bold policies and actions by both public and private actors are needed to curb this illicit trade.

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.

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 score0.230
Threshold uncertainty score0.324

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.009
GPT teacher head0.243
Teacher spread0.234 · 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