Light transmittance characterization and energy-saving analysis of a new selective coating for in situ window retrofit
Bibliographic record
Abstract
Windows are often pointed to as the weakest elements in building facades, given their low thermal resistance. Consequently, they are a main focus during building retrofits, when their substitution is often considered an expensive but necessary choice. This article describes a new high-performance glazing coating, which can be used for the in situ retrofit of existing windows. The easy application of this new liquid-applied coating on the internal side of the glazing makes it possible to reduce the solar heat gain coefficient of an existing window substantially and quickly, without interrupting the building occupancy. Experimental characterizations of light transmittance and thermal characteristics are presented in this article. Although the new coating allowed a reduction in the visible transmittance (VT) of only 0.1 compared to nontreated insulating glass units (IGUs), it proved to reduce significantly the solar heat gain coefficient (SHGC) of the investigated IGUs to values below 0.45. This article also reports the results of aging studies aiming at assessing the risks of long-term performance reductions of this new liquid-applied coating. Finally, the results of energy simulations investigating the energy-efficiency improvements when this coating is applied to different kinds of windows in Canada are reported.
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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) | 0.000 | 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 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".