Climate change risks to future sustainable fishing using global seafood ecolabel data
Bibliographic record
Abstract
Marine fisheries are an important source of food and livelihoods globally. However, climate-induced changes in marine ecosystems are affecting fish populations and sustainable fishing opportunities. By combining datasets on climate-driven changes in population distribution and biomass with Marine Stewardship Council (MSC) seafood ecolabel program data, we conduct a large-scale risk analysis of fisheries under a high-emissions scenario by mid-century. Results show that fisheries targeting tuna and billfish face the highest relative risks of management disruption, due to high exposure to stock shifts and higher governance vulnerabilities, followed by small pelagic and demersal fisheries. We analyze a subset of global fisheries with high management performance (MSC-certified), suggesting risk may be higher among non-MSC-certified fisheries. These findings provide key insights into governance priorities across diverse fisheries under climate change. They underscore the need for international cooperation, regular management reviews, and effective monitoring to be prepared for climate impacts on marine resources.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".