Evaluation of the Thermal and Structural Performance of Potential Energy Efficient Wall Systems for Mid-Rise Wood-Frame Buildings
Bibliographic record
Abstract
Approximately 30% of energy use in Canada is consumed in buildings. The largest component of this energy consumption in multifamily residential buildings is space heating. One of the primary functions of building enclosure is reducing space-heating energy. Although heat flow cannot be completely prevented, it can be controlled to reduce energy consumption, create a sustainable environment, and implement indoor human comfort. However, this can be achieved by constructing a thermally resistant building enclosure. This study aims at developing and evaluating some innovative potential energy-efficient wall systems for mid-rise, wood- frame buildings in terms of their thermal and structural performances. Regarding the thermal resistance performance, four wall systems were developed, installed in a full-scale testing house, and examined on a long-term period along with a baseline wall system. The selection of the wall systems was based on specific considerations: current practice, preliminary structural analysis, prefabricability, and expected energy efficiency. Several sensors were installed at each wall system: heat flux, thermocouple and humidity sensors. The results from the thermal analysis and the structural tests provide useful directions toward future development of energy-efficient wall systems.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 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".