Numerical simulation of spatial-temporal coupling characteristics in the evolution of urban-rural landscape
Bibliographic record
Abstract
The change of landscape pattern is closely related to the quality of ecological environment, and the study of urban and rural landscape pattern, especially three-dimensional landscape pattern, is of great significance for urban-rural integration spatial planning.Based on the theory of landscape pattern, this study constructs a numerical simulation method for the characteristics of urban and rural threedimensional landscape pattern, and explores the formation of optimization strategies for the threedimensional development of urban and rural areas.Taking Chengdu City as an example, firstly, based on multi-source remote sensing data, the landscape pattern index method and gradient analysis method are utilized to explore the spatial and temporal coupling characteristics of urban and rural three-dimensional landscape patterns.Then the CA-Markov coupling model is used to predict the landscape pattern of future land use, so as to provide a reference for decision-making.The results of the study show that the landscape type changes in Chengdu City from 2005 to 2020 are dominated by the transformation between cultivated land, forest land and construction land, and the reasons for the changes are closely related to the urban development plan.In addition, the accuracy indices of the CA-Markov model all reached more than 80%, and the simulation results were reliable.The model prediction results show that construction land and cropland are the largest transformed landscape types, with a large-scale increase in the landscape area of construction land and a large-scale decrease in the landscape area of cropland.Spatially, the degree of fragmentation of the landscape pattern in Chengdu City gradually decreases, the landscape patches are more regularized, and the overall pattern shows a highly aggregated trend.The research results of this paper can be used as a reference for the optimization policy of three-dimensional landscape pattern in urban and rural areas, and provide data support and innovative ideas for the innovative development of urban and rural three-dimensional landscapes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".