Material Flow Analysis for Assessing the Sustainability Solid Waste Management Strategy
Bibliographic record
Abstract
With increasing income levels and accelerating consumption, municipal solid waste management (MSW) has become important in developing countries. This study intends to use material flow analysis (MFA) to assess the waste management strategy in Bogor-Indonesia. Moreover, this study also determines the extent of the waste flow path and provides suggestions for improvement. Waste volume data is carried out directly for ten days, referring to Indonesian Standards (SNI 3242: 2008 and SNI 19-3964-1994) related to waste management in settlements in Indonesia. Furthermore, the waste generation data were analyzed by applying the MFA Method. Data processing using STAN (Substance Flow Analysis Version 3) software makes Material Flow Analysis (MFA) images. The data shows that the waste generated is 20 kg to 140 kg per day. The existing conditions indicate that the waste is burned or disposed of in municipal landfills. The proposed waste management strategy model reveals 30.66 tons/year of inorganic waste that can be recovered through recycling and about 20.13 tons/year. The strategy that can be done is to establish a waste bank that applies Maggot BSF cultivation to recycle the organic waste produced. Decision-makers need future studies of material flows to be able to plan for changes in waste flows.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".