MétaCan
Menu
Back to cohort
Record W4402726062 · doi:10.3390/buildings14093020

Full-Scale Comparison of Two Envelope Systems for Lightweight Wooden Framing in Cold Climates

2024· article· en· W4402726062 on OpenAlexaff
Dario Samper Hernandez, Pierre Blanchet, Masoud Dadras Chomachayi, Antoine Cogulet

Bibliographic record

VenueBuildings · 2024
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsFraming (construction)Cold climateEnvelope (radar)Building envelopeAerospace engineeringArchitectural engineeringCold formingEngineeringStructural engineeringMeteorologyPhysicsThermal

Abstract

fetched live from OpenAlex

Residential homes and apartments’ cooling and heating needs account for 63% of total building energy consumption. Improvements in the properties of building envelopes are among the best ways to reduce their energy consumption. The project’s general objective was to compare the performance of externally insulated and traditional envelopes of light wooden frame buildings at full scale. Two houses were constructed and equipped with relative humidity sensors and temperature probes to assess the physical properties of the building envelope. The first house was built according to the conventional method (insulation between the studs), and the second house was built according to the method with the insulation outside the wall (also known as the perfect wall). The results showed that external insulation effectively mitigates internal condensation risks by relocating dew points to the exterior surface, thereby enhancing structural durability and thermal stability. Thermographic imaging confirmed reduced thermal bridging and improved thermal performance in the externally insulated walls. Overall, this study supports, with a full-scale experiment, the adoption of external insulation as a viable strategy for enhancing energy efficiency, thermal comfort, and durability in residential buildings.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.134
Threshold uncertainty score0.552

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 head0.012
GPT teacher head0.255
Teacher spread0.243 · 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 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
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueBuildingsSame topicBuilding Energy and Comfort OptimizationFrench-language works237,207