Practical applicability of mathematical optimization for reservoir operation and river basin management: a state-of-the-art review
Why this work is in the frame
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Bibliographic record
Abstract
The sheer number of publications that deal with the topic of optimizing the management of river basins has grown exponentially since the early 1980s, and this growth is still on the rise. Despite this, the practical actions of most reservoir operators are still based on their gut feelings, or at best on straightforward rules that did not originate from rigorous scientific studies but are rather the result of the operator’s experience or simple spreadsheet calculations. Many publications have already pointed out the gap between theory and practice over the past few decades; however, none have so far offered clear guidelines on how to overcome this gap. This paper presents an extensive literature review to examine potential reasons for this gap. In addition to this, a numerical test problem demonstrates a novel way of using linear programming for constructing Pareto-optimal solutions for a large class of multi-objective optimization problems.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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