Adopting the Principles of Building Physics, Smart Materials and New Technologies in the Design of Energy Efficient Buildings
Bibliographic record
Abstract
Buildings account for 30 to 40% of energy use globally, with a supplement of 5 to 10% being used in processing and transportation of construction products and components. Over the past few decades, the construction sector has been under increasing pressure to improve its cost efficiency, sustainability, and capacity, pushed by the endeavour and need to face consequences of global warming and climate change. Indeed, the increased awareness of climate change and other environmental concerns are empowering innovative solutions that seek to improve the quality of life while being environmentally-friendly. It is possible to satisfy and eventually reduce the energy demands of buildings, with less-carbon intensive approaches, through advancements in the realm of building physics, smart materials and new technologies; this paper attempts to introduce the concept of smart materials and new technology and show how they are being used to achieve energy efficiency in several projects.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".