Carbon-storing Prefabricated Retrofit Panel Designs
Bibliographic record
Abstract
Abstract There is a need for proven, resilient, scalable building envelope assemblies and construction systems for rapidly decarbonizing and adapting existing Canadian houses and buildings to changing climatic conditions. NRCan’s Prefabricated Exterior Energy Retrofit (PEER) project developed and demonstrated a methodology for mass, deep building retrofit using prefabricated panels. In 2023, CanmetENERGY launched a new project to incorporate promising carbon-storing, but unproven materials into prefabricated retrofit panels. In this paper, carbon-storing materials were evaluated and selected according to a variety of criteria including lifecycle carbon, hygric and thermal properties, constructability, cost, and Canadian availability. The most promising materials were selected and integrated into a panel concept. A panel concept and associated construction details were developed and modelled for embodied carbon and thermal performance. Finally, prototypes were fabricated, and a pilot project was conducted to assess constructability. The modelled thermal resistance of the panel design ranged from RSI 4.65 to 5.04 (depending on insulation material), sufficient to achieve net-zero or passive house performance for typical, existing Canadian homes. Carbon storage potential ranged from 7.6 to 34 kg CO2e/m2. If deployed at scale, such solutions could help to offset the embodied carbon associated with construction of new homes/buildings in Canada each year.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".