MétaCan
Menu
Back to cohort

Sensitivity analysis of hygrothermal performance of wood framed wall assembly under different climatic conditions: the impact of cladding properties

2021· article· en· W4200114565 on OpenAlexaffabout
Chetan Aggarwal, Lin Wang, Maurice Defo, Hua Ge, Max Junginger, Michael Lacasse

Bibliographic record

VenueJournal of Physics Conference Series · 2021
Typearticle
Languageen
FieldEngineering
TopicHygrothermal properties of building materials
Canadian institutionsConcordia University
Fundersnot available
KeywordsCladding (metalworking)MoistureMaterials scienceParametric statisticsWater contentComposite materialStructural engineeringEnvironmental scienceGeotechnical engineeringEngineeringMathematics

Abstract

fetched live from OpenAlex

Abstract One of the parameters that influences the moisture performance of the wood framed wall assembly is the material properties of exterior cladding. The uncertainties of its properties, would result in a range of wall performance. The objective of this study was to investigate the impact of uncertainties in cladding material properties on moisture performance of wood framed wall assembly under different climatic conditions. A wood framed (2×6 wood stud) wall with exterior brick cladding was simulated assuming 1% rain leakage deposited on the exterior side of sheathing membrane. A parametric study was carried out to analyze the impact of the cladding properties on the moisture response of OSB. The simulations were conducted in five different cities located in different climate zones across Canada. The aim was to identify the most influential cladding property on the moisture response of OSB, i.e., mould growth index and moisture content, to the varying cladding properties under different climatic conditions i.e., different cities under historical and future conditions. In general, it was found that liquid diffusivity is the parameter that has the most influence on moisture response of OSB in all the five cities. Also, the significance of this influence varies depending on the climatic conditions.

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 categoriesnone
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 score0.179
Threshold uncertainty score0.527

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.030
GPT teacher head0.252
Teacher spread0.223 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations5
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Physics Conference SeriesSame topicHygrothermal properties of building materialsFrench-language works237,207