Reviewon Malaysia’s GreenRE in comparison with Singapore’s \nGreenMark and UK’s BREEAM / Halmi Zainol ... [et al.]
Bibliographic record
Abstract
The movement of green building was started after the Agenda21. International Council for Research and \nInnovation in Building and Construction highlighted the main challenges that sustainable development presents to \nthe construction industry. The Agenda had an important role in the creation of green assessment tools in building \nindustry. An international project coordinated from Canada namely GB Tool (Green Building Tool) was the first \nattempt to develop green rating tool. The awareness has emerged the implementation of various rating tools such \nas LEED, CASBEE, BREEAM and GreenMark developed by the United States of America, Japan, United Kingdom and Singapore respectively. Many rating systems can be developed in different regions that lookquite different, but \nshare a common methodology and set of terms. In Malaysia, a new set of rating tool was developed by the Real Estate and Housing Developers Association (REHDA) known as GreenRE. This paper reviews the GreenRE and compare it with GreenMark and BREEAM. Since the latter were more established, this paper will study strengths, weaknesses, gaps and issue within the GreenRE. With the outcome of this study, it may help both the authority and industry-players to prepare a comprehensive assessment tool that suit to local practice.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".