Bibliographic record
Abstract
This paper introduces the main principles and structure of the GIS model (CLUECH, Conversion of land use and its effects in China) to analyze the land use change. Through GIS modeling, this paper reveals the factors that determine the distribution of the different land use types, and special emphasis is put to cultivated land. Correlation and regression analysis are used to identify the most important explanatory variables from a large set of candidate determining factors. We found that the distribution of land use in China is best described by a combination of different biophysical and socio economic factors. Furthermore, both scale and type of the studied region can have a very important effect on the correctness of the model. The result shows that the distribution of cultivated land is strongly correlated with the distribution of population, especially with the distribution of agricultural population. This relation shows the rural character of China, where population and agriculture are strongly clustered. Other important factors explaining the distribution of cultivated land are the suitability of the soil for irrigated rice cultivation, elevation, temperature, and some hydrological conditions. This means that cultivated land is also strongly related to the suitability of the soil for agriculture. In the spatial aspect, this model reveals that the conversion of cultivated land in China will mainly happen in the transition area between the eastern farming region and the west husbandry region, because of the land suitability and ecological reasons. The main results of the CLUECH model can be judged as reasonable and applied to the policy making related land use/land cover change.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".