A comparative analysis of two building rating systems Part 1: Evaluation
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
Buildings are responsible for a significant proportion of world energy usage, raw material consumption, fresh water withdrawals, carbon dioxide (CO 2 ) emissions and municipal waste production. In recognition of these problems, buildings are increasingly being procured through green design principles, and a number of tools have been developed to evaluate their environmental performance. This paper compares the two most widely adopted schemes—the UK Building Research Establishment Environmental Assessment Method (Breeam) and the international Leadership in Energy and Environmental Design (Leed), as implemented by the Canada Green Building Council. The nature and limitations of these kinds of building rating systems are discussed and their performance is analysed by considering the way credits are allocated, their ability to be customised, the complexity involved in the assessment process and the accessibility of the information they generate. The paper indicates some limitations in current practice. Emerging trends that will shape the development of future building rating systems are discussed.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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 it