Bibliographic record
Abstract
Space Syntax techniques, is a Series of theories and methods that refers to the space phenomenology.And we can name it as one of the most important contemporary methods of space morphology.In the modern era,various methods has been used to Analysis of architectural spaces.Visual method (formal), historical and continental are some of them.Space Syntax method that is used in this research, has a close meaning tocategorizing method phrase in literature.Using of this method become widespread sinceearly seventies in Bartlett school of England.With the effort of Steadman, Bill Hillier and Julian Hanson, whom first introduced this method, a new chapter of morphology in architecture has been opened.This method is technically growing that brighten the importance of using this method.The aim of this present study is an applied research, and the modality and method of this research is descriptive-analytical.Some parts of the theoretical information collected by library research and using of documents and reports.The main goal of the researches involved with this issue is the understanding of relations in space like creating zone boarders, gradating private and public spaces.This technique is one of the useful methods for understanding the space complexity and its transformation due to the design intervention.To achieve this goals briefing charts has been used.In this research, first, the method has been described.And then, usage of space syntax in architecture.At the end, with this method, regional context of Farahzad area in Tehran will be analyzed.with The result of this research,there are someobtained suggestions that at the end, the best suggested solutionwill be offered.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.929 | 0.918 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".