Priority Analysis on the Production Layout of Potato in China
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 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 it