Priority Analysis on the Production Layout of Potato in China
Bibliographic record
Abstract
Based on the panel data 2009-2018 on 23 potato producing areas in China, this paper firstly analyzes the priority of each area in potato production layout, using the production concentration index (PCI). Then, the main factors affecting the PCI of potato were identified, and used to develop an evaluation index system (EIS) for production advantage. Through entropy method, the production advantage of each area in potato cultivation was evaluated, and ranked in descending order. Finally, the priority of each area in potato production layout was measured comprehensively, and a total of 11 areas were determined as priority areas. On this basis, several suggestions were put forward to optimize the production layout of potato in China: (1) The Chinese government should give priority to the following producing areas in the planning of potato production layout: Sichuan, Guizhou, Yunnan, and Chongqing in Northwest China; Gansu, Shaanxi, and Qinghai in Northwest China; Hebei, and Inner Mongolia in North China; Heilongjiang in Northeast China; Hubei in the winter cropping area in the south. (2) The 11 priority areas should arrange potato production as per the local situation, during the planning of crop production layout. (3) The relevant planning departments should grasp the change trend in the producing areas of potato and other water-saving crops, identify their main producing areas, and deploy water-saving crops in dry and water-deficient, which are not suitable for rice or wheat.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".