Food, trade, and the environment
Bibliographic record
Abstract
Changes in human population density relative to the distribution of agricultural production and natural resources have increased international trade in the global food system (D'Odorico et al 2018).A fundamental benefit of trade is that it can increase carrying capacities relative to local environmental limits (Porkka et al 2017).At the same time, dependence on international trade has many potential costs for the food system.For example, trade may decrease resilience of food supplies to crop failures and sudden economic or political changes taking place in key 'breadbaskets' that produce food for the export market (Bren d'Amour et al 2016, Marchand et al 2016, Tigchelaar et al 2018).Trade also distances consumers from the locations of agricultural production, displacing pollution to producing countries, and changing patterns of resource use (O'Bannon et al 2014, Dalin and Conway 2016, Nesme et al 2016).Understanding the complex interdependencies among national food supplies, international trade, and the environment is a key aspect of global sustainability.This Focus Issue contains 29 articles that collectively describe, evaluate, and synthesize diverse aspects of the relationships between trade, food and water security, and the environment at local to global scales (figure 1).The current trend to protectionist policies in many countries, including in important food producing countries like the United States, makes this collection especially timely.The papers in this Focus Issue present actionable results that provide insights which can help guide further research and inform decision-making around trade.Below we highlight two of the areas that studies in this Focus Issue contribute to understanding: resilience in the global food system and cross-scale connections.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.002 |
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".