Interval Double-Sided Fuzzy Chance-Constrained Programming Model for Water Resources Allocation
Why this work is in the frame
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Bibliographic record
Abstract
In this study, an interval double-sided fuzzy chance-constrained programming (IDFCP) approach is developed for identifying water resources allocation strategies under uncertainty. Through incorporating interval parameter programming, double-sided fuzzy programming, and chance-constrained programming into a general framework, IDFCP can effectively deal with uncertainties expressed as intervals, probability distributions, and fuzzy sets. IDFCP can also examine the risk of violating system constraints. IDFCP is then applied to water resources allocation in the middle and upper reaches of Fen River Basin that is associated with multiuser, multiregion, and multisource features. Interval solutions of the compromise decision alternatives are generated under different scenarios in association with different risk levels of violating constraints (i.e., p levels), fuzzy membership degrees (i.e., α-cut levels), credible degrees (i.e., minimum and maximum), and reclaimed water utilization ratios. Results obtained show that water availability can affect water allocation pattern and system benefit. Results are helpful for decision makers to identify desirable strategies under various environmental and system-credibility constraints in more profitable and sustainable ways.
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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 | 0.001 | 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