Research on China's Citrus Export Trade Influence Factors Based on Gravity Model
Bibliographic record
Abstract
The exporting of China's Citrus plays an important role in casting off poverty and setting out on a road to prosperity,and to smoothly build a new rural area for the vast fruit farmers.This article takes the panel date during the year of 1992 to 2007 and makes use of random effect model to practically analyze the factor of affecting the exporting of China's Citrus.The result of the study shows that there are seven factors affecting the exporting of China's Citrus,which are the GDP of importing countries,the agricultural population of our country,the population of importing countries,distance between the countries and the real rate and the relationship with ASEAN.The calculating of potential process shows that nine countries as The Netherlands,Hong Kong,Canada,Indonesia,Malaysia,Russia,Vietnam,the United States and Singapore belong to the remodel potential style,and Japan belong to the pioneering potential style,and the countries of Germany,the Philippines,Thailand New Zealand,South Korea and Australia belong to the big potential style.Responding to above,we propose a suggestion that we should take part in the regional co-operation,reduce the cost of citrus products,and improve the storage conditions and export market structure.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".