On Slowing Climate Change with Ecological, Thermo-Active Building Systems
Bibliographic record
Abstract
In the early days of energy conservation(1980s) several countries took the need for energy efficiency seriously enough to sponsor some demonstration buildings. For instance, a US university design concept was built in Regina, Canada in1978. The Saskatchewan Energy Conservation house [1s]demonstrated a new, passive technology. It had super-insulated and airtight walls, large windows on the south facade, evacuated solar pipes for domestic water heating, and a heat recovery ventilator. Despite of all the technology demonstrated there, as Bomberg et al [2] explains, the passive measures were not accepted in the Canadian marketplace because the builders modified the heating system and thereby changed the air flow pattern in the house. The 1995, German passive house was accepted in marketplace because the built system could be duplicated as it was demonstrated. In this case, saving from the elimination of expensive boiler were used to improve the level of thermal insulation and air tightness. These developments led to acceptance of a few points from building science [2-4], namely: (1) any building is a system, (2) a design team should work together starting with the conceptual stage, (3) heat, air, and moisture flows are not separable, and their interactions must be recognized, (4) excellent air tightness and a high-level of thermal insulation are required in all climates.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".