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Hygrothermal Monitoring of Two Pilot Prefabricated Exterior Energy Retrofit Panel Designs

2021· article· en· W4200198253 on OpenAlexafffundabout
Brock Conley, Mark Carver, Sébastien Brideau

Bibliographic record

VenueJournal of Physics Conference Series · 2021
Typearticle
Languageen
FieldEngineering
TopicHygrothermal properties of building materials
Canadian institutionsCarleton UniversityNatural Resources Canada
FundersNatural Resources Canada
KeywordsOriented strand boardMoistureRelative humidityConstructabilityCladding (metalworking)Expanded polystyreneComposite materialMaterials scienceEnvironmental scienceStructural engineeringEngineering

Abstract

fetched live from OpenAlex

Abstract NRCan undertook a proof-of-concept project to retrofit a small building with prefabricated wall panels in 2017 in Ottawa, Canada. The retrofit used two wall panel designs: nailbase and woodframe. The Nailbase panel consisted of fiberglass batt, an expanded polystyrene (EPS) core, oriented strand board (OSB) sheathing, a rainscreen, and cladding. The Woodframe panel also featured OSB sheathing and included a 90 mm stand-off gap filled with dense-packed, fibrous insulation. A side-by-side comparison of cost, constructability, and performance was performed. The wall assemblies were instrumented to monitor the temperature, relative humidity, and moisture content of sensitive layers. The data was used to evaluate the hygrothermal performance, moisture accumulation, and risk of associated problems such as mould growth. This paper presents the monitored hygrothermal data from 2017 to 2021, compares the two approaches and assesses their feasibility. During construction, some of the fibrous insulation may have been wetted by wind-driven snow before completion. The data showed that this moisture was able to dissipate without significant risk. The sheathing of the Woodframe panel experienced a higher peak moisture content during the dry-out period. Otherwise, both panel designs showed limited potential for mould growth on monitored surfaces over the monitored period.

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 score0.037
Threshold uncertainty score0.786

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 communication0.0000.001
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 head0.077
GPT teacher head0.253
Teacher spread0.176 · 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

Citations3
Published2021
Admission routes3
Has abstractyes

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