Buildings with environmental quality management: Part 4: A path to the future NZEB
Bibliographic record
Abstract
The previous part of this article starts 100 years ago, at the time of the humble beginnings of building science, and brings us to the current stage of the net zero energy buildings (NZEB). We see how, over the years, knowledge from the observed failures of buildings has accumulated to become the basis for current building science. The strong interactions between energy efficiency, moisture management, and indoor environment and the need for their simultaneous analysis led to the concept of environmental assessment. More than 40 years of experience with passive houses (the first 10 were built in Canada in 1977) in process that would collect those developments into the mainstream of NZEB technology permits extrapolation to the future. As the first priority, we see a need for a fundamental change in the approach to NZEB—instead of improving the separate pieces of the puzzle before assembling them, we need first to establish the conceptual design of the whole system. Only after determination of the basic requirements for each subsystem and each assembly may materials that would fulfill the specific requirements of this assembly be selected. In this design process, the actual climate and socio-economic conditions (including construction cost) vary, so we must deal with a set of design principles rather than a description of a specific construction technology. A guiding set of considerations is presented below to establish a system of environmental quality management (EQM).
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".