Knowledge-based-ness and synthesis of indigenous knowledge with climate in traditional architecture: Evidence from Naeen city
Bibliographic record
Abstract
Application of indigenous knowledge is an example of knowledge-based-ness considered as the main agenda in most current urban and climate planning as well as geographical studies.It is one of the interesting topics in research on the role of climate on human dwelling and immediate environment.Indeed, building ecology emphasizes the ability to combine climate and environmental factors and rendering them to spatial qualities and structural comfort.This paper investigates the application of indigenous knowledge and the correspondence between traditional texture of Naeen city and its climate.In this regard, the climatic conditions in Naeen were studied in terms of temperature, humidity, wind and tourist comfort index (TCI) of climatourism.The data was collected from Isfahan Meteorology Website over the period 1985-2005.Subsequently, the traditional texture of Naeen, construction materials and building styles were studied through considering the special architectural conditions and compatible materials.Eventually, the correspondence between architectural styles and climatic conditions was studied.The results show that traditional architecture based on indigenous knowledge was consistent with climatic conditions.In this regard, using such materials as mud bricks and thatch with suitable heat capacity as well as using wind catchers, high walls around houses, dome construction, southward direction of houses were consistent with east-west wind direction.In addition, concentration of the traditional texture of the city, wall thickness, corridors, long hallways, courtyard ponds and roofed alleys are evidence of the effect of climatic conditions on urban texture in order to provide comfort in different seasons.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".