Bibliographic record
Abstract
In order to prepare future green hospital architecture authentication system, this study is a comparative year report to Korean, the United States, Japanese, British, Canadian and Australian green building authentication systems. Also, the United States and Australian Green hospital authentication systems were examined, and the authentication items of hospitals were compared with those of civil architecture. Though the examination and analysis, the portion of indoor environmental quality section commonly shows the average of 20.7 percent in all 6 countries. Especially, IAQ(Indoor Air Quality) among inside IEQ(Indoor Environment Quality) is overwhelmingly much treated in Korea, the U.S.A, Canada and Australia. In Japan, heat, light and sound are the important factors for authentication evaluation, while in the U.K light are more emphasized for the authentication. ‘LEED for Healthcare' as a hospital evaluation authentication system subdivided currently most. The system includes the detailed and extensive evaluation items ranging from hospital management, traffic, emission, water resources utilization to integrated design and furnishing. These overseas systems should be carefully investigated, researched and analyzed for an appropriate improvement of domestic green hospital authentication system. Also the current evaluation method of IEQ section of Korean GBCC needs to be modified. That's why the method puts too much importance on IAQ in IEQ section.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".