Benchmarking of IRC's Advanced Hygrothermal Model hygIRC using mid- and large-scale experiments
Bibliographic record
Abstract
Recent research in the field of assessment of hygrothermal response has focused on either laboratory experimentation or modelling, but less work has been reported in which both aspects are combined. Such type of studies can potentially offer useful information regarding the benchmarking of models and related methods to assess hygrothermal performance of wall assemblies. An advanced hygrothermal computer model called hygIRC was used to assess the hygrothermal response of wood sheathing board, a keycomponent of wood-frame wall assemblies. hygIRC uses hygrothermal properties of materials derived from tests on small-scale specimens undertaken in the laboratory. The intent of this work was to obtain experimental results on the hygrothermal the response of the wood sheathing, included in both mid- and full-scale wall assemblies, when subjected to nominally steady-state environmental conditions. The drying rates of wall assemblies featuring several different membranes in contact with the wood sheathing were determined from experiments undertaken in a controlled laboratory setting. These results were subsequently compared to those obtained using hygIRC as a means of helping benchmark the simulation model. Conclusions from these studies are definite: in both mid and full-scale test, results from simulation compare very favourably with those obtained from the experiment. This realistically suggests that the model adequately emulates the hygrothermal response of specific wood-frame assembly components, such as wood sheathing, over a wide range of scales for the environmental conditions to which it was subjected in the experiments. These results further enhance confidence towards the implementation of hygIRC in broader based parametric studies.
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How this classification was reachedexpand
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) | 0.001 | 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 teacher head, 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".