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Record W4235953160 · doi:10.32920/ryerson.14657112

Effective conductivity and the impact of aging effects on foam insulations in cold climates

2021· preprint· en· W4235953160 on OpenAlexafffund
Douglas Belanger

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicHygrothermal properties of building materials
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of CanadaOntario Ministry of Research, Innovation and Science
KeywordsMaterials scienceThermal conductivityComposite materialMoistureContext (archaeology)ConductivityPolyurethane

Abstract

fetched live from OpenAlex

Understanding material thermal conductivity is fundamental in high performance building design. This property is often advertised using a single value implied to be constant, though research shows that insulating materials have an effective conductivity that changes over a range of environmental parameters, including temperature and moisture levels. Various polyurethane and polyisocyanurate materials are analyzed in order to determine how the effective conductivity is altered by accelerated aging, obtained through exposure to high temperature, moisture, and freeze-thaw cycling. The measured results are used in hygrothermal simulations to determine the assumed and actual performance of insulating materials in the context of high performance wall and roof assemblies in cold climates. Results show that effects of aging and environmental temperature have higher impacts on the performance of polyisocyanurate materials than polyurethanes. Additionally, high moisture levels contribute to lower performance in all foam materials, with open cell materials experiencing the greatest performance reduction.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.553
Threshold uncertainty score0.633

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 communication0.0000.000
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 head0.012
GPT teacher head0.258
Teacher spread0.246 · 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

Citations2
Published2021
Admission routes2
Has abstractyes

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