Estimating legal and illegal catches of Russian sockeye salmon from trade and market data
Bibliographic record
Abstract
Abstract Clarke, S. C., McAllister, M. K., and Kirkpatrick, R. C. 2009. Estimating legal and illegal catches of Russian sockeye salmon from trade and market data. – ICES Journal of Marine Science, 66: 532–545. To address concerns about the conservation of Russian sockeye salmon (Oncorhynchus nerka) in the face of potentially large-scale illegal, unreported, and unregulated (IUU) fishing activities, we estimated the quantities of sockeye caught in eastern Russia based on trade data from Japan, China, and Korea. In addition to being a fishery-independent estimate, our approach avoids reliance on Russian customs documentation that may not capture quantities of fish transhipped at sea. Using a Bayesian statistical model, we estimate quantities imported and traded in municipal markets as two separate estimates of the actual Russian sockeye catch. To estimate the “excess” catch deriving from IUU fishing operations, these trade-based estimates of catch are compared with official Russian catch figures. The results support (posterior probabilities 0.72 to >0.99) the hypothesis that there are substantial quantities of excess catch of Russian sockeye making their way to East Asian markets. In the years 2003–2005, the median quantities of annual excess catch were estimated to range from 8000 to 15 000 t, representing a value of US$40–74 million and demonstrating that actual catches are 60–90% above reported levels.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".