Long-Term Planning of an Integrated Solid Waste Management System under Uncertainty—II. A North American Case Study
Bibliographic record
Abstract
In this study, a solid waste decision support system is developed for the long-term integrated planning of waste management activities in the City of Regina. The system is based on an inexact mixed-integer linear programming model, as described in a companion paper. Interactions among various system components, objectives, and constraints are analyzed. Issues concerning planning for a cost-effective diversion program and prolongation of the existing landfill are addressed. Details related to applicability of the developed system, and interpretation of the modeling outputs is also explicated. In general, six planning scenarios are examined, covering a range of potential system conditions and waste management philosophies. Scenarios 1A to 1C are based on the current practices with the solutions serving as grounds for comparisons with other scenarios. Scenarios 2A and 2B correspond to situations when the existing landfill's life span is to be extended by 5 and 10 years, respectively, indicating that the extension options are feasible as long as a large-scale centralized composting facility is initiated by the start of period 2. If the city is targeting on the diversion goals proposed by the Regina Round Table on Solid Waste Management, scenarios 3A and 3B should be considered, where a new landfill should be located at the northern site once the existing one has been totally consumed. Scenario 3A corresponds to an aggressive diversion strategy while scenario 3B is more conservative. Based on the responses from a number of practicing waste management professionals in the City of Regina, solutions for the six scenarios provide useful decision support for planning the city's waste management system. They may help bring about more cost-effective plans for the regional waste management activities.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".