Waste Generation Modeling Using System Dynamics with Seasonal and Educational Considerations
Bibliographic record
Abstract
Effective waste management is critical to environmental sustainability and public health. Various dynamics, such as seasonal changes and waste education programs, influence solid waste generation, increasing the complexity of prediction. This is important, as the proper prediction of waste quantity is necessary to develop a sustainable waste management system. In this study, municipal solid waste (MSW) management is examined in Regina, the capital city of Saskatchewan, Canada. A system dynamics (SD) model is developed to evaluate garbage and recyclable waste generation behaviours in Regina across four seasons. Three years of Regina landfill waste generation records (2016–2018) are considered to analyze and predict seasonal waste-generation trends. The effect of various factors, such as gross domestic product (GDP), population, and education attainment on the amount of waste generation is considered in the SD model. The SD model is designed as a stock-flow diagram to illustrate the relationships between variables and predict the next three years of waste trends. This finding highlights the importance of waste education and awareness program and seasonal effects on the accuracy of SD waste modeling.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".