Illicit trade in marine fish catch and its effects on ecosystems and people worldwide
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.
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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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it