Hybrid Inexact Optimization Approach with Data Envelopment Analysis for Environment Management and Planning in the City of Beijing, China
Why this work is in the frame
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Bibliographic record
Abstract
In this study, a two-stage interval-stochastic mixed integer programming method was developed for supporting long-term planning of solid waste management in the city of Beijing, China. The developed method reflects uncertainties expressed as probability density functions and intervals, as well as offers a linkage between predefined environmental policies and associated economic implication. The method has advantages in tackling dynamic, interactive, and uncertain characteristics of solid waste management system in the city, and addressing issues regarding waste diversion and landfill prolongation. Reasonable solutions were generated for waste flow allocation and system capacity expansion. Data envelopment analysis was then utilized for analyzing these solutions under different policy scenarios. Obtained results can provide useful information and decision-support for the city's solid waste management planning. Results are valuable for adjustment of the existing waste management practice and identification of desired waste flow allocation patterns for the city of Beijing. Results also suggest that the developed method is applicable to other engineering decision-making problems.
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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