The Spatial-Temporal Evolution on County Accessibility and Economic Impact of Baoding High-Speed Railway Network
Bibliographic record
Abstract
The districts and counties in the Baoding region are the subjects of this study, with an emphasis on the accessibility and economic relations between the core city, the Xiong’an New Area, and periphery districts and counties. By taking into account intracity transportation connections and passengers’ travel behavior choices, a mix of weighted average travel time, economic potential, and spatial autocorrelation analysis methodologies is applied. The accessibility modifications and evolution of the spatial distribution pattern of economic activity in the Baoding region as a result of the high-speed railway (HSR) construction are analyzed at multiscale. The findings indicate that (1) the accessibility level of the central city, the Xiong’an New Area, and counties in Baoding has been significantly improved, and a spatial distribution pattern of rings and branches has been formed, with the Beijing-Guangzhou and Xiong’an-Kunming directions serving as axes for expansion outward. The accessibility level is high in the east and high in the northwest. (2) The accessibility level is characterized by a high eastern component and a low western component. The economic potential of the core counties along the HSR line is substantially greater than that of the periphery counties, generating a geographical pattern of “twin stars” with the central city and the Xiong’an New Area at the apex and a diminishing circle radiating outward from the center. At the same time, due to the convergence of multiple HSR line. (3) Economic activities in Baoding districts and counties spread out more and more as the reachability time range widens. The optimal radiation range is within 1 hour. Economic activities in the Baoding region are not randomly distributed.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".