공공건물 태양열 및 지열시스템 리트로핏을 위한 신재생에너지 선정 지원 툴 연구
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
Under the Korean renewable energy obligation policy, public buildings in the Republic of Korea must achieve an 18%(2016) and 30%(2020) new and renewable energy supply to total energy consumption in buildings more than 1000 m2. RETScreen and RETScreenPlus is a freely available global tool developed by the Canadian national energy laboratory to quantity energy saving from the baselines for the clean energy technologies. The RETScreen decision tools and methodology can be used by decision makers, government policy developers, architects, engineers and community leaders to evaluate and select the most effective solutions for Korean public building's renewable energy requirement. The RETScreenPlus can be used by facility operators, managers and senior decision-makers to monitor, analyze, and report on key energy performance indicators. It can be used for Monitoring, Targeting & Reporting (MT&R), Measurement & Verification (M&V) and Energy tracking. In this study, the renewable energy selection tool has been introduced for Korean public buildings and case studies were conducted. In summary, this methodology enables energy project to determine and verify renewable energy saving compared the baseline energy in a public building application.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.017 | 0.026 |
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