Approach to Location Model for Land Price of Dwelling Districts in Cities——For Example, Beijing City
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Bibliographic record
Abstract
Based on the common residential land prices data in Beijing from 1998 to 2003, multiple regression method was taken to build a land prices model in order to quantitatively analyze the impact of location factors on land price by the support of spatial analysis function of GIS. Combined with the round-city road in Beijing, this paper also put forward margin land price of round-city road as a new notion by inducting dummy variables to this model. The results shows: land prices differ markedly between different round-city road, but don't have various differences in same round-city road location commercial center is the main location factor affecting distribution of land prices the impact of subway or light rail station, highway and the main road is comparatively minor park has little impact upon land prices.
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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