ON-SITE WASTE SEGREGATION PRACTICE IN MALAYSIA: MRT POLICE QUARTER PROJECT
Bibliographic record
Abstract
In Malaysia, construction waste generation increases annually, with the majority of construction waste ending up in illegal dumping sites. Indeed, construction waste can be effectively recycled if it is segregated. Waste segregation is currently enforced and mandatory in Malaysia's states that have enacted the Solid Waste and Public Cleansing Management Act 2007 (Act 672). However, construction companies are not required to practise sustainable waste management practices such as waste segregation. In Malaysia, there has been no widely published research describing the practice of on-site waste segregation. The research aims to identify the approaches to on-site waste segregation that have been implemented and the factors that influence their implementation. The data collection method used was a case study of the MRT police quarter project in Gombak, where a literature review, site survey, and interviews were conducted. It was discovered that waste segregation had become a more integral part of routine construction activities in Malaysia. Disruption to normal site activities, management effort, and project stakeholders' attitudes are the most critical factors. In contrast, cost, sitespace, environmental confinement, and facility demand are no longer identified as factors to consider when implementing on-site segregation. Rather than that, education is now viewed as a new potential factor in these practices. The study's findings can be used to assess the state-of-the-art and effectiveness of current on-site segregation in Malaysia and develop benchmarking strategies and best practices for on-site segregation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".