Outdoor test facilities for the experimental performance evaluation of construction materials and systems: The BeTOP case
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".