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Record W3203401290 · doi:10.3390/buildings11100457

In-Situ and Predicted Performance of a Certified Industrial Passive House Building under Future Climate Scenarios

2021· article· en· W3203401290 on OpenAlexaffabout
Alison Conroy, Phalguni Mukhopadhyaya, Guido Wimmers

Bibliographic record

VenueBuildings · 2021
Typearticle
Languageen
FieldEngineering
TopicHygrothermal properties of building materials
Canadian institutionsUniversity of VictoriaUniversity of Northern British Columbia
Fundersnot available
KeywordsRoofCertificationRelative humidityEnvironmental scienceCivil engineeringPassive houseClimate zonesArchitectural engineeringEngineeringMeteorologyEfficient energy usePhysical geographyGeography

Abstract

fetched live from OpenAlex

The Wood Innovation Research Lab was designed as a low energy-use building to facilitate the construction and testing of engineered wood products by the faculty and staff of the Master of Engineering in Integrated Wood Design Program at the University of Northern British Columbia in Prince George, BC, Canada. Constructed using a 533 mm thick-wall and 659 mm flat roof assembly, it received certification as Canada’s first industrial facility built to the International Passive House standard. Temperature and humidity sensors were installed in the north and south exterior wall assemblies to measure long-term hygrothermal performance. Data collected between 2018–2020 shows no record of long-term moisture accumulation within the exterior assemblies. Data collected during this time period was used to validate hygrothermal performance models for the building created using the WUFI® Plus software. Long-term performance models created using future climate data for five cities across Canada under two global warming scenarios shows favorable results, with an increase in average annual temperatures resulting in lower average relative humidity values at the interior face of the exterior sheathing board in the exterior wall assemblies.

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)
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 score0.007
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.016
GPT teacher head0.209
Teacher spread0.192 · 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 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

Citations12
Published2021
Admission routes2
Has abstractyes

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