Double-sided fuzzy chance-constrained linear fractional programming approach for water resources management
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Bibliographic record
Abstract
A double-sided fuzzy chance-constrained fractional programming (DFCFP) method is developed for planning water resources management under uncertainty. In DFCFP the system marginal benefit per unit of input under uncertainty can also be balanced. The DFCFP is applied to a real case of water resources management in the Zhangweinan River Basin, China. The results show that the amounts of water allocated to the two cities (Anyang and Handan) would be different under minimum and maximum reliability degrees. It was found that the marginal benefit of the system solved by DFCFP is bigger than the system benefit under the minimum and maximum reliability degrees, which not only improve economic efficiency in the mass, but also remedy water deficiency. Compared with the traditional double-sided fuzzy chance-constrained programming (DFCP) method, the solutions obtained from DFCFP are significantly higher, and the DFCFP has advantages in water conservation.
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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