Strategies for LEED certified projects: the building layer versus the service layer
Bibliographic record
Abstract
The study aims to evaluate the correlation between building layer (BL) and service layer (SL) (i.e., shearing layer concept) in the (i) design of new buildings under the LEED-NCv3 rating scheme and (ii) renewal of existing buildings under the LEED-EBv3 rating scheme. To decrease the influence that green policy, which can change over several years, has on the Energy and Atmosphere category of LEED, LEED-certified projects were selected exclusively from the US over the course of a year. The total number of LEED certified projects analyzed was 594. These projects were nested into 32 groups (eight US states × two types of buildings × two LEED certification levels). In 19 of 32 groups, reciprocal negative correlations between BL and SL were observed. The following three design strategies, BL-emphasized, SL-emphasized, and random, were identified. It was concluded that LEED rating schemes should be enhanced by emphasizing long time expectancy BL design strategies.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".