QUANTIFICATION ASSESSMENT OF MUNICIPAL SOLID WASTE AS AN EVALUATION APROPOS OF SUSTAINABLE WASTE MANAGEMENT IN KUCHING
Bibliographic record
Abstract
The quantification and characterisation of municipal solid waste are indispensables for waste management forethought. This study quantified the municipal solid wastes from three principal council areas in Kuching, the capital city of Sarawak, Malaysia which are the Kuching South City Council, Kuching North City Hall and Padawan Municipal Council to evaluate and analyse the contemporary waste trend and differentiate between the waste streams. The municipal solid waste samples are amassed directly from the source location and categorised according to the socio-economic level of the sampling location sites. This study discovered that there is no significant difference in the waste composition trend generated by the residents in different residential areas. The composition of the solid wastes was found to vary in different socio-economic categories. Organic waste is found to be the highest waste component in all socio-economic groups. The top three municipal waste compositions from the residential areas are organic wastes (61.58% w/w), plastics (12.06% w/w) and nappies/sanitary napkins (11.67% w/w), which ranged from 44.57% to 72.08%. This study provides a recent waste trend database with a detailed analysis of the differences between the waste streams for sustainable waste management in Kuching.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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; 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".