Risk Informed Decision-Making Framework for Operating Reservoirs Under Flooding Conditions: Accounting for Uncertainty and Risk
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents the Risk Informed Decision-making Framework and software tool we developed that formally account for flood risk and uncertainty in reservoir operations. The framework and the software tool are intended for practical use by reservoir operations planners to manage flooding events. We present a robust and comprehensive approach that accounts for a multitude of flood risks and their impacts, and that enables its users to identify those alternative reservoir operating plans that formally adopt a state-of-the-art risk informed decision-making framework. Solidly grounded in and closely follows a well-structured planning process, the framework augments existing planning processes and information flows that incorporates specific techniques and methods from probabilistic risk analysis (PRA) and Multi-criteria Decision Analysis techniques (MCDA). Seven major hydropower companies and agencies in North America and Europe sponsored the development of the framework and the decision support tool. We present the results of a case study to illustrate the framework and the software system. We show that numerous advantages can be achieved using such tools over currently used approaches and that in the case of risky and high-impact processes, such as in the management of potentially high-consequence facilities such as storage reservoirs, management by a human operator is essential.
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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 | 0.001 | 0.008 |
| 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