Comparison of Renovation of Damaged Fabrics with Smart Growth Approach (Case Study: Renovation of Qarani Neighborhood in Mashhad)
Bibliographic record
Abstract
A special position is dedicated to preservation and reconstruction of old city centers in the world with the aim of cultural, economic, social and historical revival. Economic revival is considered as an important strategy which means boosting suitable and harmonious economic activities to both strengthening the existing activities and attracting new economic activities. Over time, the centers of cities gradually wear out and their importance and application decrease. This will lead to migration of city center residents to suburbs and consequently horizontal growth of cities. Smart growth and form-oriented regulations formulation known as transport regulations is as one of the introduced approaches in developed cities to deal with horizontal growth and urban sprawl. In the recent study, the processes of old areas reconstruction are compared to the transport criteria and indices of smart growth to deal with horizontal growth. We have attempted to make it possible to evaluate local projects and reconstruct damaged urban textures. In addition, hypotheses were examined using mean comparison test to determine the conformity of these methods with the smart growth. Results showed that the local project of reconstruction of Qarani neighborhood in Mashhad is inconsistent with the smart growth regulations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".