Strategic Environmental Assessment (SEA) in Tehran Comprehensive Plan of Transportation and Traffic: An Approach Toward Achieving Sustainable Urban Development Projects
Bibliographic record
Abstract
Believe in environmental reform is one of the sources of urban planning and probably is the most important and stable ideology of it. Urban rapid advancement in all realms especially during last two decades led various urban designs to reinforce harmony of urban development. On the other hand, emerge of some contexts such as sustainability and need of adoptability of urban designs with environmental factors caused new terms to create such as strategic environmental assessment.in this study; the origin of theoretical model of Strategic Environmental Assessment (SEA) will be presented. Moreover the practical circumstances of mentioned model in sustainable urban designs will analyzed. Therefore, the process of urban development designs can be optimized and the appropriate system to reach sustainability can be introduced. Transportation and traffic comprehensive plan of Tehran is the case study of the research due to its importance and adoptability to the contents of sustainable development. So, the technique taken to gain information, categorize their factors which affect sustainability is the Delphi technique. In addition, ICOLD Matrix is used to analyze the strategies of transportation comprehensive plan of Tehran with the factors of sustainability. The results of present research conform that the Strategies of Transportation and traffic comprehensive plan of Tehran needs refinement to reach the factors of sustainability. On the other hand, the process of SEA in planning period to achieve sustainable and optimum strategies is considered inevitable.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".