Methodology for service life prediction of window frames
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Window frames are an important element of buildings, with an enormous impact on the thermal performance and interior comfort conditions of buildings. Knowledge regarding the service life of window frames is extremely relevant, aiding the adoption of adequate solutions in the design and maintenance stages. This study proposes a methodology for the service life prediction of window frames, based on the visual inspection of 182 case studies, in-use conditions, in which the degradation phenomena and various characteristics of window frames are surveyed. This information is converted into degradation curves, which express the evolution of the degradation of window frames over time, allowing their service life to be estimated, as well as the influence of their characteristics on their durability. For aluminum and wooden frameworks, estimated service lives of 37.6 and 27.3 years are obtained. These results reveal that the window exposure conditions and the users’ behaviors have a substantial impact on the degradation of window frames.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it