The effect of the waste separation policy in municipal solid waste management using the system dynamic approach
Bibliographic record
Abstract
Aims: In the present study, Vensim was used to simulate waste management system of Tehran, the capital of Iran, with the system dynamic approach. Materials and Methods: The environmental system dynamic modeling is one of the comprehensive simulation tools capable of simulating and analyzing complex systems. In this approach, the model is developed based on the existing realities and userâ€'comments. User participation to develop the model could increase the reliability of the results. Results: The simulation results revealed good conformity with the statistical data. Waste production prediction in the model with real data was more than 95%. Moreover, the effect of applying an encouraging policy for people to separate their waste was considered. The result indicated that applying a new policy, and the economic benefit through this policy would prevent getting a loan from the government after 20 years. Conclusions: It could be concluded that public participation in waste separation was an effective policy to help in the financial independence of the municipality in terms of urban waste management. Moreover, conformity between the simulation results and real data revealed an appropriate capability of the simulated model to predict Tehran waste generation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".