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Record W4362469755 · doi:10.3390/f14040718

Evaluation of Moisture Performance of Tall Wood Building Envelope under Climate Change in Different Canadian Climatic Regions

2023· article· en· W4362469755 on OpenAlexaffabout
Maurice Defo, Lin Wang, Michael Lacasse, Travis Moore

Bibliographic record

VenueForests · 2023
Typearticle
Languageen
FieldEngineering
TopicHygrothermal properties of building materials
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsBuilding envelopeEnvironmental scienceCladding (metalworking)MoldClimate changeMoistureMeteorologyHydrology (agriculture)GeographyEcologyGeotechnical engineeringGeologyMaterials science

Abstract

fetched live from OpenAlex

A study was realized to assess the effects of historical and projected future climates on the hygrothermal performance of cross-laminated timber wall assemblies in 12 Canadian cities belonging to several climate regions and zones and for two cladding and ventilation types. Water ingress in the wall assembly was supposed to be 1% wind-driven rain (WDR), and the airflow rate in the drainage cavity was calculated using local climate data. The hygrothermal simulation results showed that under the assumption of no deficiencies allowing wind-driven rain to enter into the wall (perfect wall), there is no risk of mold growth in the future for both claddings, either vented or ventilated. Under the assumption of high moisture loads (1% WDR), the mold growth risk could increase significantly in all climate regions and cities considered. However, in those cities located in the Cordillera and Prairie regions, the increase was not found to be problematic as the maximum mold growth remained under the acceptable level, whereas for cities located in coastal and southeastern regions, the increase in mold growth risk could be considerable. The impacts of cladding and ventilation types on the relative performance of the walls varied with city location.

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 categoriesnone
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 score0.646
Threshold uncertainty score0.951

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.054
GPT teacher head0.270
Teacher spread0.215 · 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 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

Citations13
Published2023
Admission routes2
Has abstractyes

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