The evolution of international grain trade pattern based on complex network and entropy
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Bibliographic record
Abstract
Grain is the most basic material condition for human survival and development, and the structure of grain import and export trade has seriously affected national grain security. In this paper, we quantitatively analyze the evolution of international grain (maize, wheat, rice) trade patterns from 1987 to 2019 based on a complex network and entropy methods, and provide some suggestions and references for relevant countries. We measure the ranking of countries by applying various indicators from complex networks to quantify the importance of nodes in international trade networks. Then, we analyze the evolution of trade scale and community structure in different regions. Finally, we analyze the weight structure of the whole network through entropy, revealing the evolution characteristics and mechanism of the system more comprehensively. The results first show that the international grains trade network (IGTN) satisfies the scale-free properties and that global trade volumes are increasing year by year. The distribution of trade volumes in the IGTN follows the 80/20 rule, with less than 20[Formula: see text] of countries accounting for more than 80[Formula: see text] of global trade volumes. Second, countries in the Asian region have gradually increased their position in the grain trade network, while the position of countries in North and South America has declined. Third, the heterogeneity of the topology and weight structure of the international grain trade network is weakened, and the status of hub countries in the IGTN is reduced. The importance gap of nodal connections is narrowing, showing the trend of multilateralization of global trade. Fourth, the world grain export market is highly concentrated. The USA, Canada, Brazil, Argentina and Russia are the major exporters of grain.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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