Estimation of Urban Land System Stability of River Valley——A Case Study of the Four Districts in Suburbs of Lanzhou City
Bibliographic record
Abstract
The estimation of urban land system stability is based on the reasonable arrangements of urban land-use, the optimization of land structure system, and the achievement of sustainable utilization of land resources. This paper, taking Lanzhou city, the valley-basin city in Northwest China for example, introduced flow analysis and activity analysis of land utilization, and analyzed the spatiotemporal dynamic characteristics of land utilization and its driving mechanism in research areas by the measure model of land use change and principal component analysis, based on Landsat remote sensing image data. The research shows that:(1) In the research area, less unused land resources and reserved land resources, and the lower proportion of forest land, grassland and water area have the greater ecological risk;(2) Transformable relationships among the construction land, cultivated land, grassland and forest land are key relationships of the transition of land utilization, which has determined change characteristics of land utilization in the research area;(3) The land use change shows the class character. With the rapid development of social economy, the fast promotion of urbanization has increased the entropy of land system and decreased its stability.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".