A State of the Art of the Overall Energy Efficiency of Wood Buildings—An Overview and Future Possibilities
Bibliographic record
Abstract
The main goal of this study was to review current studies on the state of the art of wood constructions with a particular focus on energy efficiency, which could serve as a valuable source of information for both industry and scholars. This review begins with an overview of the role of materials in wood buildings to improve energy performance, covering structural and insulation materials that have already been successfully used in the market for general applications over the years. Subsequently, studies of different wood building systems (i.e., wood-frame, post-and-beam, mass timber and hybrid constructions) and energy efficiency are discussed. This is followed by a brief introduction to strategies to increase the energy efficiency of constructions. Finally, remarks and future research opportunities for wood buildings are highlighted. Some general recommendations for developing more energy-efficient wood buildings are identified in the literature and discussed. There is a lack of emerging construction concepts for wood-frame and post-and-beam buildings and a lack of design codes and specifications for mass timber and hybrid buildings. From the perspective of the potential environmental benefits of these systems as a whole, and their effects on energy efficiency and embodied energy in constructions, there are barriers that need to be considered in the future.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".