Production-Living-Ecology Nexus of Land-Use Functions in the Mountainous Areas: A Case Study of Chongqing, China
Bibliographic record
Abstract
As the basis of land resource allocation and land use planning, land-use functions (LUFs) are important indicators for evaluating the sustainable development of land and environments. It is essential to evaluate the LUFs of a specific area from the perspective of land-use comprehensive functions. This paper takes 38 districts and counties in Chongqing, China, as an example to construct a state-space model from three aspects, production factors, living factors, and ecological factors, which affect the functions of land resources. The paper continues to construct an evaluation index system and determine indicators by entropy methods to calculate and evaluate the spatial differentiation of land-use functions in Chongqing. The results show that the value of land-use multi-functions in Chongqing can be divided into three levels. The results are characterized by the gradual weakening of the main urban area and the polarization effect is strong. The distribution characteristics of this result are caused by the differences in traffic conditions, geographical location, the quality of natural conditions, economic levels, planning policies, etc. In the end, this paper proposes relative suggestions for the facet of eco-friendly production and development to strengthen future policy guidance. Future studies can continue to improve the index system and study the spatio-temporal effects of LUFs from a microscopic perspective to make the results more accurate.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".