Prediction of moisture response of wood frame walls using IRC's advanced hygrothermal model (hygIRC)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The main objective of this paper is to highlight the research works being carried out on the hygrothermal behaviour of building materials and wall systems at the National Research Council (NRC) Canada. The paper depicts selected results obtained from parametric studies conducted on wood frame stucco walls commonly used in Canadian climatic conditions, using hygIRC, an advanced hygrothermal modelling tool developed over the years at the Institute for Research in Construction (IRC), NRC Canada. Theassumed wall configurations, environmental conditions and material properties used in the study and the rationale behind such assumptions are highlighted. The drying potential of the composite wall systems and drying characteristics of various individual wall components are analysed. A number of parameters which influence the moisture movement to and from the wall have been selected for this study and they are : (1)Presence of ventilation cavity behind the cladding, (2) Size of the vent cavity, (3) Variation of external relative humidity (RH), and (4) Solar radiation on the exterior face of the wall. The parametric studies, based on simulation results, call upon the urgent need to verify these observations by conducting closely monitored field tests.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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) | 0.000 | 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 it