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Record W4386156089 · doi:10.32920/24033783.v1

An Investigation into the Resilience and Durability of High R-Value Exterior Insulated Wood Framed Walls in Cold Climates Assessed Using In-Situ Measurement and Calibrated Hygrothermal Modeling

2023· preprint· en· W4386156089 on OpenAlexaffabout
James E. Henderson

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicHygrothermal properties of building materials
Canadian institutionsToronto Metropolitan UniversitySciencetech (Canada)George Brown College
Fundersnot available
KeywordsCold climateResilience (materials science)Environmental scienceMoistureIn situMaterials sciencePermeability (electromagnetism)DurabilityMembraneComposite materialStructural engineeringEngineeringGeologyMeteorology

Abstract

fetched live from OpenAlex

<p>Super-insulated building envelopes are more energy efficient than Canadian building codes require but a particular design may not be resilient when subjected to different Canadian climates. This research evaluated the hygrothermal performance of a nominal 2x6 framed assembly with 75-225 mm of exterior mineral fiber sheathing insulation in climates across Canada. Variations of the design with and without variable permeability membranes were constructed and monitored. Results from climate chamber and in-situ monitoring were used to calibrate hygrothermal simulations. The research demonstrates that variable permeability membranes do not necessarily keep a wall drier than a traditional 6 mil poly vapour barrier and that the amount of exterior insulation can play a more important role in moisture management and resilience. Results from the climate chamber, in-situ monitoring and hygrothermal simulations demonstrated that the walls performed satisfactorily and all were able to dry under typical operating conditions.</p>

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)0.002
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.253
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.049
GPT teacher head0.259
Teacher spread0.211 · 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

Citations1
Published2023
Admission routes2
Has abstractyes

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