Bibliographic record
Abstract
Today, while creating a new geometry as urban geometry that has significant effects on the environment and urban climate, it has been become a design approach by planner and architects to promote environment comfort quality and to satisfy members.The main characteristic of urban geometry is as variable as sky view factor that states geometrical shape of the surface.In this study, while introducing the sky view factor as a key factor in comfort and its relationship to the other factors affecting on the microclimate along with case study on houses of Yazd, we try to indicate how to use the sky view factor in the formation of climate architecture of Yazd.In research method part, we have restricted the scope of our studies to open spaces of residential houses to limit the scope of studies as well as reduce variables.In this study, cross-sectional descriptive research method has been used.The sky view factor was measured through spherical shooting by Rayman computing program.The obtained data, as well as information aboutthe structure, included length width, height comparable basis, and the sky view factor in structure.The results of comparing the analogy between building show that this factor is considered in the design of buildings and principles of designing spaces are in a defined range.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.959 | 0.939 |
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".