Thermal Bridging and Linear Thermal Transmittance Calculations for Balconies
Bibliographic record
Abstract
The thermal performance of building envelopes can be significantly affected by thermal bridging. Thermal bridges are localized areas of high heat flow through walls, roofs, and other insulated building envelope components. Thermal bridging is caused by highly conductive elements that penetrate the thermal insulation or misaligned planes of thermal insulation (or both) within the building envelope. These paths allow heat flow to bypass the insulating layer and thereby reduce the effectiveness of the insulation. The architectural look of exposed slab edges and protruding balconies or eyebrow elements in contemporary buildings is becoming increasingly common; however, the impact of floor slab edges and balconies on the thermal performance of the building is not well regulated. In this study, the impact of adding a balcony to a wood frame wall assembly was studied experimentally. Two wood frame wall assemblies were tested in the National Research Council’s guarded hot box test facility in Ottawa, ON, in accordance with ASTM C1363-19, Standard Test Method for Thermal Performance of Building Materials and Envelope Assemblies by Means of a Hot Box Apparatus. The first test included a wall assembly without a balcony. The second test consisted of a wall assembly with a balcony. The linear transmittance value was calculated for the balcony. COMSOL Multiphysics software was also employed to model the wall assemblies, and the results from the three-dimensional simulations are included.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".