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
Purpose: The window energy rating system was developed in early 1990s and various kind of rating system has been implemented in advanced country such as Europe, Australia, Canada and the US since 2000. In Korea, the Energy Consumption Efficiency Rating Indication System has been implemented to promote supply of high efficiency window since July 2012. Normally, the window energy rating system based on heat balance which considers both thermal losses and solar heat gain is used and applied only to residential buildings. However, the system used nationally only considers thermal losses and is applied to every building regardless of its usage. Therefore, in this study, we indicated problems of domestic window energy rating system and looked for improvements. Method: We analyzed thermal performance of various windows through dynamic simulation applied to detached house and compared results with those of domestic and foreign rating system. Result : Thermal performance of south windows is more affected by SHGC than U-value, and that of north windows is also affected by SHGC a lot. The difference between the results of our study and current system is statistically significant. As a result, appropriate evaluation criteria which considers solar heat gain is required.
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.
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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.009 |
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