Aquaculture over-optimism?
Bibliographic record
Abstract
The recent rapid growth in aquaculture production reported by the United Nations Food and Agriculture Organization may have inadvertently generated what we denote here as aquaculture over-optimism. An extreme form of this is the notion that we need not worry about sustaining wild fish stocks because we can meet the global need through farming. Here we investigate whether the recent growth in aquaculture production can be maintained, and we compare aquaculture production projections with the future need for fish to find out whether aquaculture over-optimism can be justified. We show relevant evidence suggesting that aquaculture growth rates in all the cases studied have already reached their peak and have begun declining. Also, our results indicate that without wild fish, the world will face a fish food shortage of about 71 million tonnes annually by 2030, and the aquaculture production growth rate would have to be 3 times current average projected production by the FAO, the World Bank and the OECD in 2030. Finally, the current geographical distribution of farmed fish production suggests that even if aquaculture over-optimism is physically, economically, technically and ecologically feasible, its socio-economic cost to low-income coastal countries could be devastating.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".