Buildings with environmental quality management, part 3: From log houses to environmental quality management zero-energy buildings
Bibliographic record
Abstract
The discussion in this article starts in the 1920s, that is, at the time of the humble beginnings of building science and brings us to 2020s with the development of net-zero energy buildings. The knowledge accumulated by explaining observed failures in the practice of construction slowly formed a basis for moving toward a predictive capability and to an integration of modeling and testing. Furthermore, we have learned that interactions between energy efficiency, indoor environmental quality, and moisture management are so critical that the three issues must be considered simultaneously. Effectively, a change in the low energy is needed to ensure durability of materials and cost considerations for these buildings. At this stage, one could observe a clear change in the mind-set of the scientific community. Forty years after construction of the first 10 passive homes, we made a shocking observation—an adequate technology has been developed, but our lack of vision prevents effective use of this technology. Again, we need to modify our vision and change the design paradigm to balance comfort, building durability, and cost-effectiveness. If the quest for sustainable buildings is our ultimate objective, then we should learn more from the surrounding nature; termites appear to master the art of hygrothermal control better than humans because they can optimize transient conditions to maintain a stable interior comfort zone. Thus, in the article to follow a new compact building envelope design package is proposed, applicable to different climates with specific modifications of critical hygrothermal material properties. This approach is called the Environmental Quality Management. This will be the second step for a building science (physics) needed to become a leading force in the transition to sustainable built environments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".