Comparative Study of Form and Features of Courtyards in Terms of Outdoor Thermal Comfort in Two Contrasting Climates of Iran
Bibliographic record
Abstract
Courtyards as a traditional strategy were used to create a suitable microclimate for dwellers, but in contemporary architecture, inadequate knowledge of form and features of courtyards makes them deficient. This study presents practical solutions reaching optimal form and features of courtyards based on the traditional architecture of two contrasting climates of Iran. The ENVI-met.4 model was used for simulating the area percentage, water and vegetation level in a very cold climate of Ardabil and Hot-arid climate of Yazd. The simulated atmospheric parameters were imported to the Rayman1.2 to calculate PET thermal index. As the first step, 10% of the total area was advised to be considered a courtyard area for Yazd hot-arid climate, and 60% of the total area was recommended in a very cold climate of Ardabil. Next, the ratio of the water area to the total area of courtyards was simulated and the results advice, 0% of the courtyard to be considered a water area of Ardabil and 10% of the courtyard of Yazd is an optimum choice in hot-arid climate. In the final step, 20% of courtyards recommended the best condition of tree coverage in both climates. By these guidelines, designers can create a more adaptive architecture to the local climate.
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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 | 0.000 | 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) | 0.001 | 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".