Bibliographic record
Abstract
This paper analyzes the applicability and technology level of the green building element technology by analyzing the scorecard of the LEED Canada certified buildings. The results of this analysis will be able to evaluate the current status and level of application of green building element technology, and it will be used as a basic data to prepare for the revision of the certification standards and the establishment of strategies for the future green building activation. In this study, 1,010 LEED Canada certified buildings from 2011 to 2016 were analyzed. The results were analyzed by focusing on the status of greening by certification category and the characteristics of adoption by sub-category. The results of the study are summarized as follows. ① EA category showed the biggest difference between the certification scores and the adoption rates than other categories, and it was confirmed that it is not easy category from the viewpoint of practical adoption. On the other hand, the ID (Innovation & Design Process) and WE were analyzed to be relatively easy to apply, with a score of over 72%. ② In the case of Platinum class, the upper adoption rate is shown in all categories except MR. The Gold class shows the adoption rate of WE and ID in the top, and the middle rate in most categories. Silver grades showed lower rates in the three categories except WE, ID and EQ. Certified grades showed the adoption rates of the bottom except the two categories.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".