A Focus on the Contribution of Promoting TOD to Increasing Tehran's Public Spaces
Bibliographic record
Abstract
Development of underground spaces is a modern approach in urban development. Paying due attention to this issue can help improve the quality of life in Tehran's dense urban areas. A large number of Tehran's municipal districts lack adequate urban facilities and public spaces. This is while, owning land in these districts has become quite difficult and expensive. Various jobs and a large number of buildings with diverse uses have been created and constructed, respectively, around subway stations, which are centers to which people are attracted and, from which, are scattered throughout the city. Nevertheless, since most of the stations in Tehran are located in urban areas with high-density development, it is quite difficult to provide sufficient public service spaces around or adjacent to them. Transit-oriented development (TOD) is a mixed-use residential and commercial area designed and created around (within a radius of one-quarter to one-half mile from) train or subway stations as well as tram or bus stops to maximize access to public transport and create adequate space at the entrance of subway stations. With an area of 730 square kilometers and a population of about 8 million, Tehran ranks 25th in the world in terms of having the largest population in its metropolitan area. With more than 180 stations and five lines, Tehran Metro ranks 21st in the world in terms of the number of stations and lines. Most of these stations are located in the major urban centers and areas. The present study seeks to stress and clarify the importance and status of TOD approach as an effective strategy to increase Tehran's underground public spaces. In addition to exploiting Tehran's underground potentials and capacities, this approach helps resolve a number of the city's problems, such as lack of public spaces and inefficient public transportation, and improve its quality of life and environmental issues.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".