Learned Lessons from Traditional Architecture in Yemen -Towards Sustainable Architecture
Bibliographic record
Abstract
This paper explores the Learned Lessons from traditional Yemeni Architecture Towards Sustainable Architecture. It highlights how the local context influences the traditional architecture in Sanaa city and different regions of Yemen and Arab regions, according to nature, climatic conditions, culture, traditional values, and indigenous knowledge. Overview for sustainability during the twentieth century, sustainability and the Islam perspective in the Arab region, and selected the traditional architecture in Yemen as a case study. In addition to the analysis analyzed the city's urban form and the traditional house in Sana’a city, the design and elements of the house; spatial organization, construction systems and building materials, and window openings. Ornaments and sewerage systems. The study summarizes the aspects of sustainability in the traditional house in different regions in Yemen as an indigenous traditional knowledge for sustainable architecture. In conclusion, the traditional houses in the house in Yemen, designed according to the local context and indigenous traditional knowledge, have influenced traditional Yemeni architecture; the house elements and design fulfills sustainable requirements and positively impact the city's environmental, economic, and social aspects. Furthermore, it is considered a learned lesson from traditional architectural heritage and indigenous traditional knowledge toward sustainable architecture.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".