An empirical study on spatial–temporal dynamics and influencing factors of apple production in China
Bibliographic record
Abstract
In the context of supply-side structural reform, revealing the characteristics of spatial-temporal dynamics and influencing factors of China's apple production layout is of great significance to ensure apple supply and demand balance and timely adjustment of industrial policies and regional layout strategies. Based on national and provincial apple production data from 1978 to 2016, this study used the apple production concentration index to analyse the evolution characteristics of regional apple production patterns in China. A theoretical analysis framework was established and a spatial econometric model was used to quantitatively explore the influencing factors of China's apple production layout. The results showed that, first, since the reform and opening-up policy, a general trend of fluctuating growth was found for apple production in China. The centre of apple production layout moved in the southwest direction, with the shift from the Bohai Bay region to the Loess Plateau region. Second, apple production had a significant spatial correlation, while the degree of spatial agglomeration gradually decreased. Third, these changes were significantly influenced by apple comparative income, infrastructure, policies, and climatic conditions. Therefore, it is necessary to continue optimizing and adjusting the apple spatial layout to enhance the technological progress and economic effect of the apple industry and to ensure the stability and balance of regional supply and demand.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".