Illicit trade in marine fish catch and its effects on ecosystems and people worldwide
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".