“双重冲击”下世界油菜籽及其加工品生产、贸易格局变动分析Changes in production and trade pattern of global rapeseed and its processed products under the background of "double impact"
Bibliographic record
Abstract
近年爆发的贸易争端与新冠肺炎疫情，对世界油菜籽及其加工品的生产、贸易格局产生了一定影响，研究这一问题对保障中国油菜籽及其加工品进口供应链安全具有重要意义。总结分析了世界油菜籽及其加工品生产、贸易格局变动特征，实证分析了主要进出口国（地区）的显示性比较优势与贸易互补性，探讨了贸易争端与新冠肺炎疫情对世界油菜籽及其加工品生产、贸易格局的“双重冲击”。主要结论为：中长期看，“双重冲击”不会大幅改变基于比较优势的世界油菜籽及其加工品的生产、贸易格局；短期内，进出口大国之间的贸易争端将加剧国别替代，中国与加拿大、俄罗斯、澳大利亚的贸易规模将逐渐此消彼长；新冠肺炎疫情增加了贸易成本和供应链风险，对提升跨国物流与供应链绩效提出了更高要求。 In recent years, the outbreak of trade disputes and the epidemic situation of COVID-19 has a certain impact on the production and trade pattern of rapeseed and its processed products in the world. The study of this issue is of great significance to ensure the security of the import supply chain of rapeseed and its processed products in China. The characteristics of changes in the production and trade patterns of rapeseed and its processed products in the world were summarised and analysed, the demonstrated comparative advantages and trade complementarities of major importing and exporting countries(regions) were empirically analysed, and the "double impact" of trade disputes and the epidemic situation of COVID-19 on the production and trade pattern of rapeseed and its processed products in the world was explored. The main conclusions are as follows: in the medium to long term, the "double impact" will not significantly change the production and trade pattern of rapeseed and its processed products in the world based on comparative advantage. In the short term, trade disputes between major importing and exporting countries will intensify country substitution, and the scale of trade between China and Canada, Russia and Australia will one fade and the other grow. The epidemic situation of COVID-19 has increased trade costs and supply chain risks, and put forward higher requirements for improving the performance of cross-border logistics and supply chain.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".