Does aquaculture add resilience to the global food system?
Bibliographic record
Abstract
Aquaculture is the fastest growing food sector and continues to expand alongside terrestrial crop and livestock production. Using portfolio theory as a conceptual framework, we explore how current interconnections between the aquaculture, crop, livestock, and fisheries sectors act as an impediment to, or an opportunity for, enhanced resilience in the global food system given increased resource scarcity and climate change. Aquaculture can potentially enhance resilience through improved resource use efficiencies and increased diversification of farmed species, locales of production, and feeding strategies. However, aquaculture's reliance on terrestrial crops and wild fish for feeds, its dependence on freshwater and land for culture sites, and its broad array of environmental impacts diminishes its ability to add resilience. Feeds for livestock and farmed fish that are fed rely largely on the same crops, although the fraction destined for aquaculture is presently small (∼4%). As demand for high-value fed aquaculture products grows, competition for these crops will also rise, as will the demand for wild fish as feed inputs. Many of these crops and forage fish are also consumed directly by humans and provide essential nutrition for low-income households. Their rising use in aquafeeds has the potential to increase price levels and volatility, worsening food insecurity among the most vulnerable populations. Although the diversification of global food production systems that includes aquaculture offers promise for enhanced resilience, such promise will not be realized if government policies fail to provide adequate incentives for resource efficiency, equity, and environmental protection.
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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 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".