Introducing maximum sustainable yield targets in fisheries could enhance global food security
Bibliographic record
Abstract
Abstract Aquatic foods are crucial for global food and nutrition security, but overfishing has led to depleted fish stocks, threatening both food security and the environment. Here, we combine a fish stock model with a global agriculture and food market model in order to analyze scenarios involving a continuation of current fishing trends versus optimal management through maximum sustainable yield targets. Maximum sustainable yield management of overfished stocks could increase yields by 10.6 Megatons, equivalent to 12% of total catches and 6% of aquatic animal production in 2022. This would alleviate the need for aquaculture expansion by an equivalent of 3 years of growth in the aquaculture sector at its current level, and reduce meat and feed demand. Lower food prices and additional supply could enhance global food security. Conversely, continued overfishing will likely lead to lower catches over time, adding pressure to the agricultural and aquaculture sectors. Although maximum sustainable yield management is not a panacea, it represents a positive step towards achieving sustainable food production.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".