Conceptual Model for Sustainable Planning and Development of Waste Management with Material Flow Analysis (MFA) and Analytical Hierarchy Process (AHP) Methods
Bibliographic record
Abstract
The waste management in the Bogor Region, particularly concerning the Galuga Landfill, requires a thorough assessment of its operational efficiency and environmental impacts to address the complex issues it presents.Despite ongoing efforts to enhance waste management techniques, significant shortcomings remain in the effectiveness of the current system.This study aims to propose a viable solution to mitigate the issue of overcapacity at Galuga Landfill, hence prolonging its operational lifespan.This study utilizes material flow analysis (MFA) in conjunction with other methodologies, particularly the analytical hierarchy process (AHP), which is especially relevant in solid waste management (SWM).The study on the waste management system design in the Bogor Region indicates that the technical elements of waste management have not adopted the concepts of integration and sustainability, as per the study's findings.This is evident in the phases of waste management, which include sorting, processing, transportation, and ultimate disposal.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".