Coordinative Urban‐Rural Solid Waste Management: A Fractional Dual‐Objective Programming Model for the Regional Munifcipality of Xiamen
Bibliographic record
Abstract
A linear fractional programming based solid waste management planning model was proposed and applied to support the planning of urban‐rural solid waste management in Xiamen, China. The model could obtain the best system efficiency while solving the tradeoff between economic and environmental objectives. It aimed to effectively address the urban and rural solid waste management planning through minimizing the system cost and optimizing system efficiency in the developed model framework. Through the model, the optimal waste flow for each facility was obtained, and the problem of overburdened landfill in Xiamen’s urban and rural solid waste management system was solved. The solutions for waste allocation and facility capacity expansion were provided for Xiamen’s urban and rural solid waste management. The planning results showed that about 42.44 × 106 tons of waste would be diverted to other facilities from landfills over the planning period of 2018‐2032, and the waste diversion rate would reach 97%, which would greatly reduce the burden on landfills. The economic efficiency of waste diversion would be 5.07 × 103 ton per 106 RMB. All the capacities of Xiamen’s urban and rural solid waste management facilities including incinerators, composting plant, and landfills should be expanded because of increasing waste production rate.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.004 | 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".