Analysis of the Impact of Japan's Positive List System on Chinese Mushrooms
Bibliographic record
Abstract
Since Japan implemented the positive list system in 2006, The amount and quantity of mushroom exported from China to Japan are decreasing. The export of mushroom in China has been hit hard. This article uses panel data for each quarter of China from 2005 to 2018.By constructing the corresponding virtual variable measurement model, the paper analyzes the impact of Japan's implementation of the positive list system on China's export of mushrooms to Japan. Empirical results show, China’s GDP and the exchange rate of RMB against Japan have a positive correlation with the export value of shiitake mushrooms, but the impact is not significant. However, the market price and positive list system of shiitake mushrooms have a negative correlation with the export value of shiitake mushrooms. The higher the price of mushrooms, the smaller the export value, and the stricter the system will also reduce the export volume, and the impact of these two factors is more significant. Comprehensive research, from the aspects of enterprise management model, the role of industry associations and the coordination of management functions of the government, we propose relevant countermeasures to promote China's export of Japanese shiitake mushrooms under the affirmative list system.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".