Assessing the hygrothermal response of wood sheathing and combined membrane-sheathing assemblies to steady-state environmental conditions
Bibliographic record
Abstract
The hygrothermal performance of building envelope system is dictated by the response of the system to combined heat, air and moisture fluctuations produced by exterior and interior conditions that exist on either side of the envelope. This study was undertaken to generate information that would assist the benchmarking hygIRC model and related methods to assess hygrothermal performance of wall assemblies. This paper reports on experimental results to assess the drying rate of various combinations of sheathing membrane in close contact with wood-based sheathing. A weighing system was devised to conduct drying experiments on wood based products that would retrieve data on the change in weight of specimens continuously over time from which the hygrothermal response of the specimens could then be determined. Specimens consisting of saturated wood sheathing board products of approximately 0.8-m by 1-m size and weighing 5-kg, were either wrapped in different types of sheathing membrane or directly exposed to uniform environmental conditions in a climate chamber. Results provide a measure of the hygrothermal response of the different combinations of sheathing and sheathing membrane and establish rates at which drying occurs in these products. The results are useful in helping benchmark mathematical hygrothermal simulation model hygIRC.
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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 | 0.000 | 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) | 0.002 | 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".