Spatial Modeling of Land Use and Its Effects 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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it