Estimates of illegal and unreported fish in seafood imports to the USA
Bibliographic record
Abstract
Illegal and unreported catches represented 20–32% by weight of wild-caught seafood imported to the USA in 2011, as determined from robust estimates, including uncertainty, of illegal and unreported fishing activities in the source countries. These illegal imports are valued at between $1.3 and $2.1 billion, out of a total of $16.5 billion for the 2.3 million tonnes of edible seafood imports, including farmed products. This trade represents between 4% and 16% of the value of the global illegal fish catch and reveals the unintentional role of the USA, one of the largest seafood markets in the world, in funding the profits of illegal fishing. Supply chain case studies are presented for tuna, wild shrimp and Chinese re-processed Russian pollock, salmon and crab imported to the USA. To address this critical issue of unintended financing of illegal fishing, possible remedies from industry practices and government policies may include improved chain of custody and traceability controls and an amendment to the USA Lacey Act.
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 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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".