Applying Zero Waste Management Concept in a City of Indonesia: A Literature Review
Bibliographic record
Abstract
City waste management in Indonesia still faces loads of challenges, mainly in the case the ultimate disposal (landfill) availability. Only 60-70% of the waste can be transported and disposed of to landfill, while the rest are scattered in various places. Waste dumped in landfill emits leachate contaminating and greenhouse gases. Also, the discarded material is a waste of non-renewable natural resource. Holistic management is necessary, starting upstream to downstream waste management. The concept of Zero Waste offers waste management, initial from the avoiding of trash, recycling, reduction and recovery of second-hand material. Some cities in the world such as Canberra, Adelaide (Australia), Stockholm (Sweden), Nova-Scotia (Canada) and San Francisco (USA) has set a target of Zero Waste. Indonesia still implements management that emphasizes the waste management disposal in a landfill. This literature review examines to find out whether Indonesia can apply the concept of Zero Waste in the upcoming.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".