A Review of Municipal Waste Management with Zero Waste Concept: Strategies, Potential and Challenge in Indonesia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Municipal waste management is still a significant problem for solid waste issues in Indonesia. Only 60 to 70% of the waste generated is disposed of in landfills, the rest is dispersed in different areas. The potential for leachate pollution, greenhouse gases, and a waste of non-renewable natural resources can occur due to municipal waste management problems not being optimal. Municipal waste management needs a holistic concept that would include upstream to downstream stages. This paper comprehensively reviews municipal waste management with a zero waste concept based on management, development, measuring, implementations, strategies, potentials, and challenges in Indonesia. The zero waste concept offers waste management, starting with waste elimination, recycling, reduction, and recovery of used goods. Several municipalities around the globe, such as Canberra, Adelaide (Australia), Stockholm (Sweden), Nova-Scotia (Canada), and San Francisco (United States), have decided on targets for zero waste cities. Indonesia is still implementing waste management that accentuates disposal in landfills, so there needs to be a literature study related to the management, development, measuring, implementations, strategies, potentials, and challenges of Indonesia’s zero waste concept.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it