In-Situ Dew-point Measurement to Assess Life Span of Insulating Glass Units
Bibliographic record
Abstract
Replacement of insulating glass (IG) units in buildings is expensive. Replacement costs can be estimated fairly accurately. However, timing is less certain. In the author's experience, time estimates for replacement are often based on poor understanding of the causes of IG unit “failure” (water vapour condensation on glass surfaces facing the IG unit cavity) and previous negative experience, and thus are reactive rather than predictive. The life span of insulating glass units in service is not well known. Insulating glass units have been made in North America since the late 1950s. Laboratory test methods developed in Canada in the late 1950s and early 1960s, subsequently used as the basis of most IG unit test methods worldwide, were intended to assess the likelihood of successful performance through the IG unit manufacturer's warranty period, not to determine service life span. In the 1980's, based on in-situ testing for the “Field Correlation Study” by the Sealed Insulating Glass Manufacturers Association (SIGMA) in the USA, Spetz proposed that one component of the laboratory test method, dew-point measurement of cavity gas fill, could be used to estimate time to failure of IG units in service. This technique is examined in this paper and modifications are suggested to improve accuracy.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".