LEED, Energy Savings, and Carbon Abatement: Related but Not Synonymous
Bibliographic record
Abstract
E fforts to green the building industry are strongly linked to performance metrics such as Leadership in Energy and Environmental Design (LEED).Yet, despite the importance of climate impact as a driver of green building choices, such standards typically address a basket of poorly quantified "green" attributes and, when they do address energy, they focus on conservation rather than carbon emissions so that that they do not provide an efficient guide to managing the climate impact of our building stock.Standards such as LEED will enable more cost-effective emissions reductions if they distinguish carbon footprint from energy use, avoid arbitrary costineffective assumptions about best paths for decarbonizing electricity, and close the loop by requiring objective measurements of real-world building performance so we can learn by doing.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.015 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| 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".