On the Effects of Variation of Thermal Conductivity in Buildings in the Italian Construction Sector
Bibliographic record
Abstract
Stationary and dynamic heat and mass transfer analyses of building components are an essential part of energy efficient design of new and retrofitted buildings. Generally, a single constant thermal conductivity value is assumed for each material layer in construction components. However, the variability of thermal conductivity may depend on many factors; temperature and moisture content are among the most relevant ones. A linear temperature dependence of thermal conductivity has been found experimentally for materials made of inorganic fibers such as rockwool or fiberglass, showing lower thermal conductivities at lower temperatures. On the contrary, a nonlinear temperature dependence has been found for foamed insulation materials like polyisocyanurate, with a significant deviation from linear behavior. For this reason, thermal conductivity assumptions used in thermal calculations of construction components and in whole-building performance simulations have to be critically questioned. This study aims to evaluate how temperature affects thermal conductivity of materials in building components such as exterior walls and flat roofs in different climate conditions. Therefore, experimental conductivities measured for four common insulation materials have been used as a basis to simulate the behavior of typical construction components in three different Italian climate conditions, corresponding to the cities of Turin, Rome, and Palermo.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".