The Impact of Urban Rail Transit on Surrounding Residential Prices--Line 1 of Chengdu Metro as an Example
Bibliographic record
Abstract
This article aims to build a hedonic price model of the research region by making a summary of relevant research in this field, and taking the running Line 1 of Chengdu Metro as an example. To be specific, the research discusses the influence of Line 1 of Chengdu Metro on surrounding residential prices taking samples of the estates within 2 km of Line 1 within a spatiotemporal perspective. The results shows that: after the operation of Line 1 (from July in 2010 to June in 2011), the growth rate of the residential prices has been increased by 5.89%, 9.44%, 12.45% and 11.03% from 1st ring to the third ring respectively, reaching at an average rising rate of 9.51%. Besides, the residential prices far away from the downtown area were more likely to be found ahead than those around the city center and represented a far more sensitivity to the metro operation. By quantitative calculating, it finds that: regions that are closer to subway stations per meter from 1st ring to the third, the residential prices increased 0.91 Yuan/m2, 1.16 Yuan/m2 and 1.21 Yuan/m2, respectively. At the same time, Line 1 of Chengdu Metro has been increased a total number of 7.814 billion Yuan up to now of surrounding residential value.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".