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Record W4319455708 · doi:10.1016/j.cscm.2023.e01920

Outdoor test facilities for the experimental performance evaluation of construction materials and systems: The BeTOP case

2023· article· en· W4319455708 on OpenAlexafffundabout
Umberto Berardi, Shahrzad Soudian

Bibliographic record

VenueCase Studies in Construction Materials · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicWind and Air Flow Studies
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsScale (ratio)Relation (database)Computer scienceFull scaleArchitectural engineeringTest (biology)Systems engineeringReliability engineeringEngineeringCivil engineering

Abstract

fetched live from OpenAlex

Proposing new materials and systems to improve the performance and energy efficiency of buildings is often followed by performance evaluation to monitor how they perform and contribute. Experimental performance characterization of new and existing building materials and systems is crucial to understanding their behaviour in relation to indoor environmental and conditional changes, in addition to outdoor environmental changes. Full-scale experimental test cell facilities have been at the forefront of experimental performance evaluation in building-related research, as they can provide a realistic representation of buildings, which includes environmental conditions, building structure, and operational characteristics. In this paper, the new test cell facility of BeTOP, located in the city of Toronto, Ontario, is introduced as a full-scale experimental facility with the capability of multiple practical tests simultaneously. This paper describes the characteristics of this test cell, including structure details, testing capabilities, system details, previous testing campaigns, and future testing potential. The design of such a full-scale testing facility is shown to be crucial in a continental climate, such as Toronto, to observe the long-term performance of new systems under variable boundary conditions with cold winters and hot and humid summer seasons.

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)0.002
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 score0.513
Threshold uncertainty score0.640

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
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 head0.076
GPT teacher head0.326
Teacher spread0.250 · 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

Citations7
Published2023
Admission routes3
Has abstractyes

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