CEDES: A Complete, Legitimate and Seamless Green Building Rating System
Bibliographic record
Abstract
This paper proposes a new more complete, rigorous and seamless Green Building Rating System (GBRS) than the currently used systems, known as CEDES (Comprehensive Environmental Design and Evaluation System). The objective is to design a new green building rating system (GBRS) that addresses the shortcomings of existing GBRSs. To this end, a literature review was conducted, and critiques of current GBRSs were compiled. Secondly, a hierarchical structure of evaluation categories and indicators was designed, based on a comprehensive life cycle analysis of all materials and processes used in the construction sector. The result is the CEDES system. CEDES was designed from a general taxonomic conceptual framework, so it serves both as a system of sustainable evaluation, and as a general framework to create new GBRS adapted to any environmental and socio-economic environment. The novelties and contributions of this work are: 1. A GBRS has been designed that can be used internationally (CEDES); 2. CEDES can be adapted to any environmental and socioeconomic setting by simply modifying the weight of the indicators; 3. CEDES is complete, i.e. there are no missing categories or indicators as in many existing GBRS, 4. None of its categories or indicators can be considered superfluous; 5. The relative weight of each indicator is justified and legitimized and is determined by the rest of the indicators and by a complete life cycle assessment (LCA) of all aspects of the construction process; 6. CEDES serves both to evaluate buildings and as a guide to building design with the maximum ecological and sustainable level.
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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 | 0.021 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".