Bibliographic record
Abstract
In wall systems, airspaces can increase thermal resistance if a reflective material such as foil with low emissivity is installed in a furred-airspace assembly. In this article, the present model, hygIRC-C, was used to investigate the steady-state thermal resistance of wall assemblies that incorporate foil adhered to expanded polystyrene foam in a furred assembly. To investigate the effect of the furring orientation, the furring was installed horizontally and vertically and compared to walls with no furring. For wall with vertical furring, the three-dimensional version of the present model was used to capture the three-dimensional effect of the thermal bridges. Because the foil emissivity can be affected by dust accumulation and/or water vapor condensation on the foil surface, consideration was given to investigate the effect of both varying foil emissivity and outdoor temperature on the thermal performance of the various wall specimens. The results showed that the thermal resistance ( R-value) of the reference wall (no furring) is greater than the wall specimens with furring. Also, the results showed that the contribution of the furred-airspace assembly to the R-value of wall specimen with vertical furring is higher than that for wall specimen with horizontal furring.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".