Interval-parameter chance-constrained fuzzy multi-objective programming for water pollution control with sustainable wetland management
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
Water pollution control plays a significant role in the water quality management of wetland ecosystems. In this study, an interval-parameter chance-constrained fuzzy multi-objective programming (ICFMOP) model for assisting water pollution control within a sustainable wetland management system under uncertainty was developed. The proposed ICFMOP approach not only effectively handled the uncertainties and complexities in the water pollution control management systems, it also allowed decision makers to adjust the fuzzy objective control decision variable to satisfy multiple holistic and interactive objectives. The ICFMOP model developed was then applied to a wetland water pollution control case study to assist the planning of regional wetland eco-environmental sustainability. Interval solutions of the compromise decision alternatives associated with different risk levels of constraint violations were obtained. The results were helpful for decision makers to identify desirable strategies under various social-economic, environmental and system-reliability constraints with the highest system benefits and the lowest water pollutant discharge and eco-environment impact. Moreover, tradeoffs between the multiple objectives and the constraint-violation risks could be evaluated.
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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 | 0.001 |
| 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