Improved Policies for Solid Waste Management in the Municipality of Hamilton‐Wentworth, Ontario
Bibliographic record
Abstract
Abstract Public planning formulation can prove especially complicated when system components contain considerable degrees of uncertainty. Earlier research had demonstrated the utility of using evolutionary simulation‐optimization (ESO) for solid waste planning in the Municipality of Hamilton‐Wentworth, Ontario. This paper demonstrates how both penalty function minimization and grey programming (GP) can be integrated into ESO in order to efficiently generate multiple good policy alternatives that meet the Municipality's required system criteria for solid waste management. Since ESO techniques can be adapted to problems in which many system components are stochastic, the practicality of this approach can be extended into many other operational and strategic planning applications containing significant sources of uncertainty. Résumé La formulation de la planification publique peut se révéler particulièrement compliquée quand les composantes du système sont porteuses de degrés considérables d'incertitude. La recherche antérieure a démontré l'importance de l'utilisation de la méthode dite evolutionary simulation‐optimization (ESO) dans la gestion des déchets solides dans la municipality de Hamilton‐Wentworth (Ontario). Le présent article met en évidence la façon dont la minimisation de la fonction de pénalité et la programmation grise (GP) peuvent être intégrés à ESO afin de produire efficacement de bonnes solutions de rechange politique qui satisfont la grille de critères en vigueur dans la municipalité. Étant donné que les techniques d'ESO peuvent être adaptées aux problèmes dans lesquels les composantes du système sont stochastiques, le caractère pratique de cette approche peut être appliquée dans beaucoup d'autres domaines de planification stratégique et opérationnelle porteurs de multiples sources d'incertitude.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".