Comparative Analysis of Soybean Quality Standards in Asia Pacific Region(亚太地区大豆质量标准对比分析)
Bibliographic record
Abstract
Soybean is one of the main food crops planted in the world, and becomes an important food trade variety due to its richness of fat and protein. In 2018, the global soybean production had reached 366 million tons, and 80% of which was concentrated in the United States, Brazil and Argentina. In order to promote the interconnection of food standards of all economies and elevate the facilitation and integration of food trade in the Asia Pacific region, a systematic analysis of 12 economies was conducted on the soybean standards of 10 APEC economies including China, the United States, Australia, Canada, Japan, South Korea, Mexico, the Philippines, Thailand, Chinese Taipei and 2 major soybean producing countries including Brazil and Argentina. The similarities and differences applicability of standard, product classification, grading and quality indicators related to grade parameters were proposed to promote the interconnection of APEC soybean standards, which could provide reference for grain workers engaged in grain standardization, quality testing and soybean import and export trade.(大豆是世界各国主要种植的粮食作物之一,因富含脂肪和蛋白质使其成为重要的粮食贸易品种。2018年全球大豆产量已达到3.66亿t,其中美国、巴西、阿根廷3个经济体的大豆产量共占全球总产量80%左右。为推动亚太地区各经济体粮食标准互联互通,促进亚太地区粮食贸易便利化和一体化,对包括中国、美国、澳大利亚、加拿大、日本、韩国、墨西哥、菲律宾、泰国、中国台北等10个亚太经合组织经济体及巴西、阿根廷等2个大豆主产国,共12个经济体的大豆标准进行了系统分析；找出标准适用范围、产品分类、等级划分和等级参数有关质量指标等方面的相同和差异之处,对促进亚太地区大豆标准互联互通提出了建议,可为从事粮食标准化、品质检验、大豆进出口贸易的粮食工作者提供参考。)
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".