Comparative Study on Performance of Wind-Catcher Shading Device and Other Types of Shading Device on Residential Houses in Tropics
Bibliographic record
Abstract
The optimal shading system plays a significant role in a sustainable environment in controlling the amount of excessive sunlight and reducing the thermal discomfort of occupants. External shading device requires many design considerations such as solar altitude, control strategies, and aesthetics to control solar gain, improve the visual environment and reduce glare. Proper external shading design can reduce cooling energy consumption and prevent an overheated indoor environment. This paper focuses on the performance of the Wind-Catcher shading device on the residential house in a hot and humid climate. This study focused on the effect of using Wind-Catcher shading devices on indoor thermal comfort. It proposes solar shading and wind catching design and its performance compared to other types of external shading devices. One of the residential houses in a hot and humid climate is taken as the representative study house of the research. The theoretical and analytical approach will analyze three types of shading devices, such as overhang, box-type shading devices, and wind-catcher shading devices, to validate and compare the shading magnitude amount. The result indicates that the Wind-Catcher shading significantly reduces thermal discomfort hours and allows moderate wind flow into the room rather than other shading types.
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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) | 0.002 | 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".