Global marine fisheries discards: A synthesis of reconstructed data
Bibliographic record
Abstract
Abstract As part of the global marine fisheries catch reconstruction project conducted by the Sea Around Us over the last decade, estimates were derived for discards in all major fisheries in the world. The reconstruction process derives conservative but non‐zero time‐series estimates for every fisheries component known to exist, and relies on a wide variety of data and information sources and on conservative assumptions to ensure comprehensive and complete time‐series coverage. Globally, estimated discards increased from under 5 million t/year (t = 1,000 kg) in the early 1950s to a peak of 18.8 million t in 1989, and gradually declined thereafter to levels of the late 1950s of less than 10 million t/year. Thus, estimated discards represented between 10% and 20% of total reconstructed catches (reported landings + unreported landings + unreported discards) per year up to the year 2000, after which estimated discards accounted for slightly less than 10% of total annual catches. Most discards were generated by industrial (i.e. large‐scale) fisheries. Discarding occurred predominantly in northern Atlantic waters in the earlier decades (1950s–1980s), after which discarding off the West Coast of Africa dominated. More recently, fleets operating in Northwest Pacific and Western Central Pacific waters generated the most discards. In most areas, discards consist essentially of marketable taxa, suggesting a combination of poor fishing practices and poor management procedures is largely responsible for the waste discarding represents. This is important in an era of increasing food security and human nutritional health concerns, especially in developing countries.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".