Investigation of Thermal Performance of Structural Insulated Panels for Northern Canada
Bibliographic record
Abstract
The thermal performance of structural insulated panels (SIP) and connections, developed and used to build 142 homes in Nunavut, Canada, was studied by subjecting the panels to steady-state cold climate conditions in a laboratory test setup. Testing was carried out using an inverted test hut, in which the panels were installed such that the interior of the hut was cooled down to outdoor conditions, and the ambient lab conditions served as the indoor climate. This inverted setup provides an alternative to using a large-scale environmental chamber when this is not available. Results showed the methodology used in this test is adequate to characterize the thermal performance at both the center of the panel and the connections. In carrying out steady-state thermal simulations on both the panel and connection cross sections using both one-dimensional (1D) and two-dimensional (2D) programs, it was found that while the 1D simulation could adequately predict the performance at the center of the panel, a 2D simulation was required to predict the performance at the connections. The SIPs themselves were found to provide good thermal performance.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".