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Record W2617761755 · doi:10.1201/9781003078852-60

Assessing the hygrothermal response of wood sheathing and combined membrane-sheathing assemblies to steady-state environmental conditions

2020· book-chapter· en· W2617761755 on OpenAlexaff
Wahid Maref, Michael Lacasse, D.J. Booth

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEngineering
TopicHygrothermal properties of building materials
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsMaterials scienceSteady state (chemistry)Composite materialChemistry

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.099
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.021
GPT teacher head0.234
Teacher spread0.213 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2020
Admission routes1
Has abstractyes

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