Hygrothermal Analysis and Prediction of Long-Term Thermal Performance of Aerogel-Enhanced Superinsulation Products
Bibliographic record
Abstract
Aerogel-enhanced insulating materials provide significantly higher thermal resistance per unit of thickness compared to conventional insulating materials. These superinsulation materials are relatively new in the construction industry, and their thermal properties under different hygric conditions and their durability are still unknown. In this study, the main characteristics of a variety of aerogel-based materials under different climatic conditions were studied. Furthermore, the samples underwent accelerated aging tests, and the impact of aging on the thermal performance of aerogel-based products was investigated. The results showed that the thermal properties of aerogel-based products are affected by moisture content and their thermal resistance degraded under extreme humidity conditions. The analysis of the aging effects showed that aerogel-based products would maintain their superior thermal performance over time. The average increase in the thermal conductivity (compared to the pristine condition) was below 10% after the equivalent of twenty years of aging under various climatic factors.
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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 | 0.000 |
| 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) | 0.001 | 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".