Fisheries Centre research reports. Volume 31, number 2
Bibliographic record
Abstract
This technical report estimates the catch loss arising from overfished fish stocks and the socio-economic impacts associated with this catch loss. In this analysis, catch loss is defined as the difference between the maximum sustainable yield of a fish stock and its catch in the most recent year. We focus on 482 fish stocks identified as ‘overfished’ in the Global Fishing Index, a global assessment of the sustainability of over 1,400 fish stocks conducted by the Minderoo Foundation. For these overfished stocks, we estimate (1) the potential catch loss (in tonnes) from overfished fish stocks; (2) the landed value (in USD) of catch loss; and (3) the number of jobs associated with marine fisheries catch loss worldwide. Our results indicate that the annual estimated catch loss for 482 overfished fish stocks amounted to 15 million tonnes worldwide. This catch loss results in huge societal cost, translating to around US$39 billion in potential lost landed value annually, and an estimated 668,479 associated full time equivalent jobs. If all the overfished fish stocks were fished at MSY, all regions worldwide could potentially gain fishing jobs, with highest potential gains in Latin America and the Caribbean (24% above current jobs). Thus, our analysis emphasises the urgent need to immediately rebuild overfished fish stocks in order to recoup the current economic and social benefits that are forgone with catch loss.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.279 | 0.198 |
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".