Investigating the service life of high-performance glazing systems
Bibliographic record
Abstract
Between energy-efficient building programs and rising urban temperatures, finding building envelope solutions that could be used to both decrease the operational carbon associated with space conditioning as well as decrease the risk of overheating within the residential sector is of interest. High-performance glazing solutions such as vacuum-insulated glazing (VIG) have emerged as a pivotal advancement in energy-saving technology, offering high center-of-glass thermal resistance, thin profiles, and weight savings as compared to conventional glazing systems. However, without a means of evaluating the minimum service life of any insulated glazing unit (IGU), let alone VIG, subsequent life cycle analysis and maintenance schedules cannot be performed or created, thus limiting our understanding of whether the technology could be considered a low-carbon alternative to conventional window technology. This paper offers a comprehensive overview of current fenestration evaluation standards, test procedures, and equipment essential for measuring performance metrics required for the development of an appropriate accelerated ageing protocol to evaluate the durability, long-term performance, and service life of high-performance glazing systems post-initial certification. In addition, this paper proposes a series of tests that will be evaluated for their potential to estimate the service life of VIG as compared to double and triple-glazed IGU. The outcome of this work could lead to a better understanding of how both comfort parameters and performance metrics associated with our choice of glazing technology may change over their service life, potentially providing the necessary catalyst for the uptake and adoption of high-performance glazing technology going forward.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".