[논문] 교육시설물의 LEED 인증유무에 따른 공사비 비교연구
Bibliographic record
Abstract
The efforts for sustainable development in building construction is widely applied by global organizations, governments, etc. However, according to the researchers, if the green rating systems on the building, it is reported that construction costs and durations are increased compared to conventional buildings. In this respect, the objective of this study is to identify the construction costs between LEED and non-LEED buildings. The scope of this study is limited in 21 university buildings of Canada. The methodology is as follows: First, the data of LEED and non-LEED buildings are collected in every university building. Second, the average construction costs per square meter is collected and normality check is conducted. Third, to identify statistical significance, the difference of average construction costs is analyzed by using T-test. As a result, it is concluded that the construction costs of LEED buildings are increased by approximately 3.8% more than non-LEED buildings. In the future, the results of this study can be applied to analyzing the additional costs according to the LEED grade in educational buildings.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".