Policy Planning Using Genetic Algorithms Combined with Simulation: The Case of Municipal Solid Waste
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Previous research had introduced a genetic algorithm procedure for creating alternative policy options for municipal solid waste (MSW) management planning. These alternatives were generated during the design phase of planning, with the final policy determined in subsequent comparative analysis. However, because of the many uncertain factors that exist within MSW systems, this earlier procedure cannot be applied to situations containing such stochastic components. In this paper, it is shown that a generic algorithm approach can be simultaneously combined with simulation to incorporate these stochastic elements in the policy option generation phase; thereby permitting uncertainty to be directly integrated into the construction of the alternatives during the planning-design phase. This procedure is applied to case data taken from the Regional Municipality of Hamilton–Wentworth in the Province of Ontario, Canada. It can be shown that this procedure extends the earlier approach and provides many practical planning benefits for problems when uncertain conditions are present.
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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 it