Blue food demand across geographic and temporal scales
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Numerous studies have focused on the need to expand production of 'blue foods', defined as aquatic foods captured or cultivated in marine and freshwater systems, to meet rising population- and income-driven demand. Here we analyze the roles of economic, demographic, and geographic factors and preferences in shaping blue food demand, using secondary data from FAO and The World Bank, parameters from published models, and case studies at national to sub-national scales. Our results show a weak cross-sectional relationship between per capita income and consumption globally when using an aggregate fish metric. Disaggregation by fish species group reveals distinct geographic patterns; for example, high consumption of freshwater fish in China and pelagic fish in Ghana and Peru where these fish are widely available, affordable, and traditionally eaten. We project a near doubling of global fish demand by mid-century assuming continued growth in aquaculture production and constant real prices for fish. Our study concludes that nutritional and environmental consequences of rising demand will depend on substitution among fish groups and other animal source foods in national diets.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it