The comparison of spatial characteristics in urban landuse growth among the central and sub-cities in Shanghai Region
Why this work is in the frame
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Bibliographic record
Abstract
By using multi temporal remotely sensed data of TM ETM, the spatial behavior of urban growth in Shanghai Region was studied by establishing and applying the urbanization metrics (i e UPI UII) in GIS buffering analysis, which was also used in comparing and analysing the spatio temporal changes in urban landuse growth of central and sub cities of Shanghai The results showed that: 1) Being without influence of large scale geomorphic heterogeneity except geo contrast between the ocean and terrene, the spatial behavior of urban landuse expanding is largely regulated by the distance to the Shanghai central city (i e CBD) Urban landuse expanding exhibited the distinctive spatial characteristics in different periods, and the activity spatial distribution of urban expanding circle also showed their unique traits in different periods. 2) The urban landuse growth presented obvious trends in directional variation The overall directional variation within 10 km to the CBD is dominated by spatial heterogeneity in central urban landuse growth, whereas the distribution and the variation in growth rate of sub cities play the key role in overall directional heterogeneity beyond 10 km to the CBD, and the small scale geomorphic variation from the spatial pattern of rivers and channels also shows its contribution. 3) Shanghai central city keeps overwhelming preponderance to the sub cities in magnitude, intensity and potential of urban landuse growth Affected by the location and sociao economic condition, the main sub cities performed differently in their spatial behavior of urban landuse growth individually, and thus can be classified into four categories according to their performance in urban landuse expanding (i e Standard, Passive, Steady and Irregular types)
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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