Influencing Factors for Developing Underground Pedestrian Systems in Cities
Bibliographic record
Abstract
Underground pedestrian systems (UPS) have been developed worldwide especially in the central areas of mega cities. They are integrated with subway systems, underground shopping streets and malls, and the basement of department stores in various forms and integrated with commerce, transport, retailing and public usage in urban functions. In cities with severe weather conditions such as Toronto and Montreal in Canada, UPS provided a weather-controlled walking environment. In dense urban settings such as Tokyo, Japan and Shanghai, China, UPS provides a comprehensive usage of urban space that is comparable to that which occurs at the street level. The natural and built environments affect the utilization of UPS. Environmental factors are discussed to demonstrate how UPS have developed and functioned. Based on previous research, this paper has selected 19 cities as cases studies to explore the decisive factors of natural and built environments that have influenced UPS development specifically with regard to four aspects namely climate, subway construction, land usage and economic environment. The research revealed the extent of prevalence of these four aspects in cities and determined the differentiating factors of the natural and built environments that resulted in the establishment of UPS. SPSS was applied to test the differences between developing and advanced economies in relation to the prevalence of these factors. 1.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".