Urban Expansion Analysis of Hefei City in the Last 30 Years by Using Romote Sensing
Bibliographic record
Abstract
By using six images from Landsat MSS/TM/ETM + dates in 1981a,1989a,1995a,2000a,2005a and 2010a,combined with evaluation of urban expansion,Land Use/Land Coverage Change and urban heat island,we analysis spatial and temporal different characteristics in urban expansion of Hefei City,the driving forces and resultant impacts on ecological environment in the last 30 years.The results showed that during the period from 1989 to 1995,urban land-use expansion in Hefei City showed slow growth trend,the rest of the time were a period of rapid growth.The relationship between population growth and urban land-use in Hefei City is more reasonable,and the development of the city has been the strong support of the population.The development of urban land-use expansion is from compact and stable to loose and complex.Urban expansion by concentric expansion,the center of gravity is basically stable into a cluster expansion,the center of gravity is significantly transfer to south and west.Economic development,population growth,traffic traction and government decision are driving urban land-use expansion.Urban heat island effect in the area as increas as the urban expansion,but the intensity of the heat island showed weakening trend,the decrease in high temperature zone and very high temperature area,showing the trend of multi-center and film distribution.The occupation of the main type of land use is agricultural land in urban land-use expansion,the area decreased by 23.61% from 1995 to 2010.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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".