Comparison Between Experimental and Simulated Hygrothermal Response of Chopped-Straw- and Cellulose-Insulated Wood Frame Panels
Bibliographic record
Abstract
Achieving a decarbonized built environment in Canada requires proven, resilient, and scalable building envelope assemblies. In 2022, building operations accounted for 18% of Canada’s greenhouse gas (GHG) emissions, with space heating responsible for nearly two-thirds of this total. Alongside operational carbon reductions, embodied carbon emissions—stemming from the production and transport of building materials—must be prioritized during the design phase. Without intervention, construction materials could consume up to half of the remaining global 1.5 °C carbon budget by 2050. This paper highlights NRCan’s prototype, low-carbon, prefabricated panels filled with chopped straw and cellulose insulation under the Prefabricated Exterior Energy Retrofit (PEER) research project. The research advances confidence in performance and durability of biogenic materials by conducting controlled experiments, guarded hot box testing, and hygrothermal modelling. These panels present a promising pathway to drastically lower embodied carbon in the built environment. The validated hygrothermal model, accurate to between 3% and 7, enables assessment of hygrothermal performance across Canadian climates, retrofit scenarios and future climate conditions. This work supports the evidence for low-carbon or bio-based materials as a solution for Canada’s built environment.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".