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Record W4285732402 · doi:10.3390/buildings12071031

Parametric Study of Lightweight Wooden Wall Assemblies for Cold and Subarctic Climates Using External Insulation

2022· article· en· W4285732402 on OpenAlexafffund
Alexis Caron-Rousseau, Pierre Blanchet, Louis Gosselin

Bibliographic record

VenueBuildings · 2022
Typearticle
Languageen
FieldEngineering
TopicHygrothermal properties of building materials
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSubarctic climateSoundproofingCold climateSound transmission classParametric statisticsStructural engineeringMaterials scienceComposite materialEngineeringCivil engineeringMeteorologyGeology

Abstract

fetched live from OpenAlex

While externally insulated wall assemblies are widely recognized for their hygrothermal performance, few research projects have focused on the impact of shifting the entire wall insulation to the exterior side of a structural cavity in cold or subarctic climates or its effectiveness in terms of acoustic performance and airtightness. The objective of this study was to propose fully externally insulated assemblies that could be used in cold and subarctic climates by assessing the benefits of the hygrothermal performance of these assemblies and by achieving a comparable airtightness and sound transmission performance to the modern assemblies that are currently built in North America. The results suggested that the externally insulated assemblies limited the risk of condensation occurring inside structural cavities and allowed for faster drying than the modern assemblies when exposed to water infiltration or high water contents in all climates that were tested. The assemblies with external airtight insulation boards were more airtight than assemblies with air barrier membranes and, in addition, assemblies with external soundproof insulation were shown to be necessary to achieve a comparable sound transmission loss to that of a modern assembly.

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 score0.269
Threshold uncertainty score0.612

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)0.0000.000

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 head0.029
GPT teacher head0.249
Teacher spread0.220 · 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

Citations10
Published2022
Admission routes2
Has abstractyes

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