Vulnerability of blue foods to human-induced environmental change
Bibliographic record
Abstract
Abstract Global aquatic or ‘blue’ foods, essential to over 3.2 billion people, face challenges of maintaining supply in a changing environment while adhering to safety and sustainability standards. Despite the growing concerns over their environmental impacts, limited attention has been paid to how blue food production is influenced by anthropogenic environmental changes. Here we assess the vulnerability of global blue food systems to predominant environmental disturbances and predict the spatial impacts. Over 90% of global blue food production faces substantial risks from environmental change, with the major producers in Asia and the United States facing the greatest threats. Capture fisheries generally demonstrate higher vulnerability than aquaculture in marine environments, while the opposite is true in freshwater environments. While threats to production quantity are widespread across marine and inland systems, food safety risks are concentrated within a few countries. Identifying and supporting mitigation and adaptation measures in response to environmental stressors is particularly important in developing countries in Asia, Latin America and Africa where risks are high and national response capacities are low. These findings lay groundwork for future work to map environmental threats and opportunities, aiding strategic planning and policy development for resilient and sustainable blue food production under changing conditions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".