Thermal Performance Analysis of Exterior Wall Materials of Huizhou Residential Buildings Adapted to Local Climate
Bibliographic record
Abstract
The exterior wall, essential to the envelope structure of Huizhou residential buildings, enjoys a unique ability to adapt to the local environment and climate.However, there is a lack of experiments and quantitative data on the thermal performance of exterior wall materials, owing to the complexity in the current situation of Huizhou residential buildings.Therefore, the purpose of this study is to explore the physical properties and energy transfer features of the exterior walls of Huizhou residential buildings.Specifically, the thermal performances of four types of walls of Huizhou residential buildings, with different materials and structures, were compared through field survey, field test and lab test.The principal conclusion is that the heat transfer coefficients of the four types of walls, namely, the traditional rowlock cavity wall made from black bricks and yellow mud, the new rowlock cavity wall with cement bricks, the new composite wall of Hongcun Village, and the solid wall of self-built houses in Jicun Village, were respectively, 1.892 W/m 2 •K, 2.821 W/m 2 •K, 2.024 W/m 2 •K and 3.588 W/m 2 •K; the traditional rowlock cavity wall made from black bricks and yellow mud boasted the best thermal performance.The research findings lay the basis for the development of ecofriendly walls that adapt to the local climatic environment.
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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 | 0.001 | 0.001 |
| 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".