Estimating illegal and unreported catches from marine ecosystems: a basis for change
Bibliographic record
Abstract
Abstract To evaluate the impacts of fishing on marine ecosystems, the total extraction of fish must be known. Putting a figure on total extraction entails the difficult task of estimating, in addition to reported landings, discards, illegal and unmandated catches. Unreported catches cast various types of shadow, which may be tracked and estimated quantitatively. Some shadows of unreported catches are reviewed, for example, an innovative, well‐funded NGO publicizes illegal catch in the Southern Ocean. For various reasons, official figures often have the implicit but unacceptable assumption that such categories are null. We present an estimation procedure based on adjustment factors taken from observer reports, correspondents and published information that track changes in a regulatory regime, and hence reflect incentives and disincentives to misreport. Monte Carlo simulations address uncertainty using multiple sources of information to provide upper and lower estimates. Once in place, this method provides preliminary estimates that may be refined without disruption. The method is demonstrated for fisheries in Iceland and Morocco. We use a ‘by‐species’ approach for Icelandic cod and haddock, while the Moroccan catch is divided into demersal and pelagic categories. Results suggest that Icelandic cod catches may have been underestimated by between 1 and 14% at different times, and haddock by between 1 and 28%. Underestimation of Moroccan catches appears to have been as much as by 50%. These case studies show that it is possible to obtain estimates of misreporting, even when direct data are lacking. Our method encourages transparency because sources of information are presented so that uncertain values are easily identified, offering a basis for comment, collaboration and refinement in estimating illegal and unreported fishing.
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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 | 0.022 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".