QUANTITATIVE ANALYSIS OF URBAN EXPANSION IN CENTRAL CHINA
Bibliographic record
Abstract
Abstract. Quantifying urban expansion forms is important to understanding regional urbanization processes and urban planning. For this purpose, conventional landscape indices are commonly used for quantitative analysis of urban landscape patterns. However, these landscape indices only reflect information for one particular temporal phase of landscape patterns. This paper studies and quantifies the dynamic changes of urban landscape from 1993 to 2006 in Changsha-Zhuzhou-Xiangtan metropolitan areas in Hunan province of China using landscape expansion index (LEI), which contains information of the formation processes of landscape patterns. The results indicate that there are three types of urban expansions: infilling, edge-expansion and outlying in the study area. The change of proportion of the three urban expansion types reveals that urban expansion patterns have changed from a messy, dispersed early development phase to more compact and reasonable layout from 1993 to 2006. Moreover, the urban expansion modes varied in different periods. From 1993 to 1996, the edge-expansion and outlying were the main types of urban expansion forms, indicating an early stage of rapid urban developments. Comparing with the edge-expansion, the outlying expansion increased rapidly in this period, which indicates urban development is messy and dispersion. Overall, the edge-expansion was the major type of urban expansion form during the study period with outlying as the second and rapidly-increasing major form of expansion prior to 1998, which indicates urbanization is in the early stage of rapid urban developments, and infilling as the second and rapidly-increasing major form of expansion after 1998.
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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 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".