Urban form Analysis Based on Smart Growth Characteristics at Neighborhoods of 9th District in Mashhad Municipality
Bibliographic record
Abstract
The purpose of this article is investigating the form of cities based on the new approach of urban smart growth and transect. Currently, the smart urban growth by using the transect method has been able to apply the environmental criteria and keep away from sprawl. The design is based on applying transect method in scale of neighborhoods within metropolis zone. In the zones of transect, different indexes of urban forms have defined clearly; furthermore are measurable and analyzable. The second purpose is determining degree of compatibility between urban characteristics within metropolis zone in one hand, and form-base codes of smart growth in the other hand. The case study of present research is selected due to having diversity of urban forms, different kinds of density, land-use and urban natural landscapes. For this diversity, 9th district in Mashhad metropolis was selected. The transect method has six separate zoning from T1 as the most natural and rural indexes, to T6 including most urban and dense indexes. The new method of Space Matrix for measuring the urban is used for transect zoning. By selecting a north- south crosscut in the considered district and exploiting the urban indexes, the Transect typology of each selected urban unit was determined by spacematrix method. Then, resulted indexes for each urban unit separately were assessed by multi-criteria decision-making matrixes(MCDM). Finaly by the hypothesis test part, with respect to compliance of more than 50% of 26 indexes of urban units of 9th district, it seems that direction of new urban regulation and models totally express avoiding sprawl and tending to ecologic approaches in the concepts of smart growth and urban form characteristics can analyzed.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".