Aesthetic Preferences for Visual Quality of Urban Landscape in Derak High-Rise Buildings (Shiraz)
Bibliographic record
Abstract
The purpose of this study is to extract the indices of Aesthetic preferences for visual quality of urban landscape in high-rise buildings which contribute designers to make better decisions for designing urban landscape. As the research focuses on the high-rise buildings, this study goal address the question as follows: ‘What are aesthetic preferences in high-rise buildings? How can these preferences be developed and categorized?’ To achieve this objective, the Derak district of Shiraz city was selected as a case study area using Photo grid method and then all high-rise buildings in this area were identified and analyzed. Aesthetic preferences data were evaluated by Q-SORT method with the psychophysical approach. Eventually, aesthetic factors have been presented in two main categories: 'primary and distinctive'. Findings lead to the development of APPD model which suggests that when the landscape design of a building moves toward distinctive factors, the degree of its aesthetic preferences increases.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".