The Influence Factors Decomposition of Grain Output Increase in China: 2003-2014
Bibliographic record
Abstract
As the world’s most populous country taking up 18.84% of the world’s total population, China’s grain supply has attracted high concerns in the world. This article takes the grain output increase in China from 2003 to 2014 as the research object, and wants to explore the influence factors of China’s grain growth and spatial effect analysis. It employs LMDI method to decompose China’s total grain output increase during 2003-2014. The increase of OFPPU paid maximum contribution to China’s total grain output increase among ACL, MCI and GPP. The conclusion of China’s grain output provincial spatial analysis is that the six provinces including Heilongjiang, Henan, Inner Mongolia, Jilin, Anhui and Shandong, contributed 62.2% share to China’s increase. The results of predicted OFPPU by Exponential Smoothing Method of Hoter-Winter No-Seasonal are that grain growth in China can meet requirements of grain security for China’s population growth and living improvement. The author wishes China can increase investment on land consolidation and renovate facilities for farmland and water conservancy projects.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".