MétaCan
Menu
Back to cohort
Record W7010461923

Influence of material properties on the moisture response of an ideal stucco wall : results from hygrothermal simulation

2002· article· en· W7010461923 on OpenAlexvenueaboutno aff

Bibliographic record

VenueNPARC · 2002
Typearticle
Languageen
FieldArts and Humanities
TopicReformation and Early Modern Christianity
Canadian institutionsnot available
Fundersnot available
KeywordsMoistureComponent (thermodynamics)Relative humidityWork (physics)Material propertiesHumidity
DOInot available

Abstract

fetched live from OpenAlex

In a composite wall, each constituent component plays a specific role in effective moisture management. The moisture movement to and from the wall assembly, subjected to climatic forces, such as relative humidity (RH), temperature, rainfall and solar radiation, is influenced by the basic hygrothermal properties of the component materials. Studies conducted at the National Research Council (NRC) Canada, over the years, show that the hygrothermal properties of materials vary over a wide range. Quite naturally, such variation may influence the overall moisture response of the wall. This paper investigates the extent to which each component of the wall can influence the overall drying and wetting characteristics of an ideal (i.e., without any deficiency) wood-frame stucco wall when it is exposed to weather conditions typical of coastal Western Canada. The component materials selected include stucco, sheathing board, a sheathing membrane and a vapour barrier. Hygrothermal material properties compiled in the laboratory at the NRC and the hygrothermal modelling tool, hygIRC, developed by the NRC, are used for this purpose. The findings of this study indicate that, with further work and analysis, practical guidance could be developed to assist designers and engineers to identify the critical components and properties of the stucco wall assembly to achieve a desirable moisture management strategy for the building envelope.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.562
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.046
GPT teacher head0.224
Teacher spread0.179 · 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 designSimulation or modeling
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

Citations2
Published2002
Admission routes2
Has abstractyes

Explore more

Same venueNPARCSame topicReformation and Early Modern ChristianityFrench-language works237,207