Quantification of air leakage effects on the condensation resistance of windows
Bibliographic record
Abstract
Windows are rated for their different performance parameters so that they could be evaluated either for adequacy to perform in specific applications, or to meet certain requirements. The rating of fenestration products also provides a means to compare products for selection purposes.Test methods are already established to determine almost all the performance factors to assess window characterization in controlled environment. However, there is some disagreement about the testing protocol of windows for condensation resistance. There is a debate on whether windows should be tested with sealed or unsealed cracks.This paper provides a discussion on how air leakage through the cracks can affect the test results when testing windows for condensation resistance. The intent is to determine the effects of the window design and the selection of components, such as weather-stripping, hardware and other components, on its condensation resistance. These effects would be masked when the cracks are sealed to prevent cold air from flowing through them, and hence changing the test results. This paper also provides test data to confirm the notion that when testing windows for condensation resistance, they should be tested without sealing any cracks in the assembly other than the interface between the window and the surround panel where it is installed.
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 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".