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Record W2609817400 · doi:10.4224/20378979

Drying experiment of wood-frame wall assemblies performed in the climatic chamber EEEF: specification of equipment used in EEEF - Environmental Exposure Envelope Facility

2002· article· en· W2609817400 on OpenAlexvenueno aff
Wahid Maref, Dennis Booth, Michael Lacasse, M. Nicholls

Bibliographic record

VenueNPARC · 2002
Typearticle
Languageen
FieldEngineering
TopicHygrothermal properties of building materials
Canadian institutionsnot available
Fundersnot available
KeywordsEnvelope (radar)Frame (networking)Environmental scienceEnvironmental chamberNuclear engineeringWaste managementEngineeringMechanical engineeringAerospace engineeringChemistry

Abstract

fetched live from OpenAlex

IRC's Building Envelope and Structure Program has a new and unique test facility that incorporates a computer automated environmental chamber with a weighing system for full-scale wall assemblies (2.43 m x 2.43 m), climate sensors, data acquisition systems and post-processing tools. The climatic chamber, which is unique in North America, is known as Envelope Environmental Exposure Facility (EEEF). It can simulate interior and exterior climatic conditions over an extended period of time, controlling both temperatures (ranging from -47° to +48° C) on the " weather" side of the wall and humidity levels ranging from 10 to 100% RH. Researchers, in conjunction with key industry partners, are using the facility to benchmark the thermal and moisture performance of walls in various climates, and the interfaces between walls and a) other building elements (such as windows) and b) at the penetrations. The EEEF has been used to gather key information regarding the rate of drying of specific wood-frame wall components when subjected to simulated rainfall. The development of the EEEF has been an on-going effort within the program to help address issues regarding effective moisture control in the building envelope.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.035
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.000

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 head0.036
GPT teacher head0.222
Teacher spread0.187 · 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

Citations5
Published2002
Admission routes1
Has abstractyes

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