Development of rating system for Sustainable building in Malaysia
Bibliographic record
Abstract
Existing environmental assessment methods attempt to measure improvements in the environmental performance of buildings relative to current typical practice or requirements. The assumption is that by continually improving the environmental performance of individual buildings, the collective reduction in resource use and ecological loadings by the building industry will be sufficient to fully address the environmental agenda. The choice of the term ‘green building assessment ’ is seen as a useful term to convey this intent. Several environmental methodologies and methods for evaluating environmental performance of buildings are being currently developed. In a global scale it is worth mentioning SB (Sustainable Building) Tool, formerly known as GB Tool (Green Building Tool) which is an international project coordinated from Canada, LEED (Leadership in Energy and Environmental Design) a method developed in the USA with a world wide application and CASBEE (Comprehensive Assessment System for Building Environmental Efficiency), a method developed in Japan. In Europe, some of the most frequently used include BREEAM (Building Research Establish Environmental Assessment Method) in the UK and also it is worth mentioning the HQE (High Environmental Quality) developed in France during the last decade and the VERDE method developed recently in Spain (Maria Sinou 2006). In this paper several method will be discuss and will become main references for developing Sustainable building Tool for Malaysia.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".