Inexact Multistage Stochastic Quadratic Programming Method for Planning Water Resources Systems under Uncertainty
Bibliographic record
Abstract
An inexact multistage stochastic quadratic programming (IMQP) method was developed for supporting water resources management under uncertainty. The IMQP method improves upon the existing multistage programming and inexact quadratic programming approaches, and can directly tackle uncertainties presented as interval numbers and probability distributions within a multistage context. Moreover, it can accommodate real-time dynamics of system uncertainties based on a complete set of scenarios; it can also deal with non-linearities in the objective function to reflect the effects of marginal utility on system benefits and costs. Because penalties are exercised with recourse against any infeasibility, the IMQP can support the analysis of various policy scenarios that are associated with different levels of economic consequences when the promised water-allocation targets are violated. The developed method was applied to the planning of water resources management in the Heshui River Basin, China. The results are useful for generating decision alternatives that correspond to various system conditions and for water resources managers.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".