China's distant‐water fisheries in the 21st century
Bibliographic record
Abstract
Abstract We conservatively estimate the distant‐water fleet catch of the People's Republic of China for 2000–2011, using a newly assembled database of reported occurrence of Chinese fishing vessels in various parts of the world and information on the annual catch by vessel type. Given the unreliability of official statistics, uncertainty of results was estimated through a regionally stratified Monte Carlo approach, which documents the presence and number of Chinese vessels in Exclusive Economic Zones and then multiplies these by the expected annual catch per vessel. We find that China, which over‐reports its domestic catch, substantially under‐reports the catch of its distant‐water fleets. This catch, estimated at 4.6 million t year−1 (95% central distribution, 3.4–6.1 million t year−1) from 2000 to 2011 (compared with an average of 368 000 t·year−1 reported by China to FAO), corresponds to an ex‐vessel landed value of 8.93 billion € year−1 (95% central distribution, 6.3–12.3 billion). Chinese distant‐water fleets extract the largest catch in African waters (3.1 million t year−1, 95% central distribution, 2.0–4.4 million t), followed by Asia (1.0 million t year−1, 0.56–1.5 million t), Oceania (198 000 t year−1, 144 000–262 000 t), Central and South America (182 000 t year−1, 94 000–299 000 t) and Antarctica (48 000 t year−1, 8 000–129 000 t). The uncertainty of these estimates is relatively high, but several sources of inaccuracy could not be fully resolved given the constraints inherent in the underlying data and method, which also prevented us from distinguishing between legal and illegal catch.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".