Bibliographic record
Abstract
In 1902, Mozafaredin Shah was familiar with the car on his trip to Europe and brought two cars to Iran. Gradually more cars were brought into the country. Since 1921, the body of Iran's major cities was affected by movement of vehicles. Nowadays, the unbridled use of cars affects Iran's urban management, particularly in cities with a population of more than fifty thousand persons. In addition to the many advantages, the vehicle presence in Iranian cities sometimes has negative consequences. Traffic, noise and air pollution are inevitable disadvantages. Lack of public and private parking is a serious issue for those involved in planning and urban management in Iran, leading to social and economic costs. Lack of parking in Iranian cities is affected by cultural and economic factors and administrative structures. The creation of public parking will not be improved without modification of the factors. Hence, this study introduces a new idea based on the reform approach which resolves a significant part of the problems. This idea is based on the revision and reform of registration rules. A corrective action in the event of operational supports leads to improving the construction of public parking. This new idea provides the severance of public parking and the possibility of issuing the entire document for each unit of parking.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".