An Evolutionary Grey, Hop, Skip, and Jump Approach: Generating Alternative Policies for the Expansion of Waste Management
Bibliographic record
Abstract
Evolutionary simulation-optimization methods are combined with a Grey Hop, Skip, and Jump (GHSJ) approach in an application to municipal solid waste management planning. GHSJ techniques have been effectively applied to problems containing uncertain information. Simulation-optimization methods can be adapted to a wide variety of problem types in which some or all of the system components are stochastic. In this paper, the advantages from both of these techniques are combined and used for efficiently generating improved decision alternatives. An illustrative application of the method is provided to demonstrate the usefulness of this approach in the planning design phase for the expansion of a waste management system. By using this approach, multiple different planning alternatives can be created that meet established system criteria, while simultaneously remaining acceptable and implementable in practice. Solid waste decision makers faced with difficult and controversial choices would then interpret and analyze these alternatives to internalize the environmental-economic tradeoffs prior to selecting their final policy.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".