A Constraint-Softened Interval-Fuzzy Linear Programming Approach for Environmental Management Under Uncertainty
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Bibliographic record
Abstract
In this study, a constraint-softened interval-fuzzy linear programming (CS-IFLP) method is developed for violation analysis of environmental management systems under uncertainty. CS-IFLP can deal with uncertainties presented in terms of fuzzy sets and intervals. Moreover, a number of fuzzy relaxation levels for system constraints are allowed, such that the relevant decision space can be expanded. This can help generate a range of decision alternatives under various system conditions, and facilitate in-depth analyses of tradeoffs among economic objective, satisfaction degree, and constraint-violation risk. The developed method is applied to a case study of long-term municipal solid waste management planning. Results indicate that reasonable solutions for both binary and continuous variables have been generated. A higher relaxation level could result in a lower system cost and a higher satisfaction degree, but with a higher constraint-violation risk. Results of the sensitivity analyses demonstrate that violated system constraints have various effects on the system cost and satisfaction degree.
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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