FISH AS FOOD: PROJECTIONS TO 2020 UNDER DIFFERENT SCENARIOS
Bibliographic record
Abstract
This paper reports results of incorporating fish into IMPACT, a global model of food supply and demand that estimates market-clearing prices to 2020 for 32 commodities in 36 regions. It summarizes results for production, consumption, net exports and real price changes for 10 economic categories of fisheries items, disaggregated into 15 geographic regions of the world. Under the medium-variant scenario for the uncertain capture fisheries sectors, global production of food fish is projected to rise by 1.5% annually through 2020, with two-thirds of this from aquaculture, whose share in total food fish production rises to 41%. Global per capita fish consumption is projected to be 17.1 kg in 2020, with sensitivity analysis indicating a margin of 2 kg/capita either way based on extreme scenarios for capture and aquaculture. Most growth will occur in developing countries, which will account for 79% of food fish production in 2020. China's share of world production will continue to expand, while that of Japan, the EU, and former USSR will continue to contract. Real fish prices will rise 4 to 16% by 2020, while meat prices will fall 3%. Fishmeal and oil prices will rise 18%; use of these commodities will increasingly be concentrated in carnivorous aquaculture. Growing domestic demand will dampen fish exports from developing countries. Sensitivity analysis incorporating a very pessimistic view of capture fisheries leads to escalating food fish prices (+69% for high-value finfish) and soaring fishmeal prices (+134%), whereas an optimistic view of increased investment in aquaculture lowers real prices of low value food fish (-12%), and raises fishmeal prices (+42%).
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".