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Record W4251455204 · doi:10.32920/ryerson.14653413

Vapour Diffusion Open Arctic Wall: A Comparison of Moisture Accumulation Potential Versus Other Cellulose Superinsulation Strategies

2021· preprint· en· W4251455204 on OpenAlexaff
Cory Brun

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicHygrothermal properties of building materials
Canadian institutionsToronto Metropolitan UniversitySciencetech (Canada)
Fundersnot available
KeywordsEnclosureArcticDiffusionMoistureEnvironmental scienceThe arcticWater vaporMaterials scienceCondensationDurabilityMeteorologyEnvironmental engineeringComposite materialEngineeringGeologyThermodynamicsGeography

Abstract

fetched live from OpenAlex

Superinsulation is becoming increasingly attractive in the construction of energy efficient new homes or energy retrofit projects. By increasing the thermal insulation inside walls, new possible unforeseen building durability issues arise that were otherwise not present during standard 2”x6” construction, as there is less potential for drying. The reduced drying is often attributed to using low permeance materials in the building enclosure. One method to combat the reduced drying potential is to use the highest permeable vapour diffusion open materials for all building enclosure components such as the “Arctic Wall”. The purpose of this study is to determine how the Arctic Wall performs in Fairbanks, Alaska in addition to other climates, and how it also compares with other common vapour diffusion open methods. The results of experimental simulation using WUFI 5.2 hygrothermal software have shown that all vapour diffusion open walls have a potential for condensation that is most dominated by the heating load across the climates that were tested. The Arctic Wall was found to be safe to use in all climates using the tested methods, but still poses a potential risk due to potential condensation due to air leakage. The results of this study have shown that the Arctic Wall performed on par with other vapour diffusion open strategies.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.108
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.065
GPT teacher head0.315
Teacher spread0.250 · 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
Published2021
Admission routes1
Has abstractyes

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Same topicHygrothermal properties of building materialsFrench-language works237,207