Designing decision-relevant partitions of the exposure space for the adaptation of reservoir operating policies under climate-change uncertainty
Bibliographic record
Abstract
The multifunctionality of reservoir systems highlights the importance of effective planning and management, which heavily depend on hydrological information. However, climate change introduces significant uncertainty, making it challenging to adapt reservoir operations based on future hydrological conditions. This manuscript compares two approaches to support adaptation planning: the cluster-specific policy approach and the influence zone approach. The cluster-specific approach assigns the same policy to hydrological scenarios with similar hydrological characteristics. The assessment of the performance of each policy within the cluster scenarios allows an estimation of the system’s flexibility. In contrast, the influence zone approach combines logistic regression with a heuristic pairwise classification to identify hydroclimatic conditions where a given policy will likely perform best according to a pre-selected performance metric. We compared the impact of both approaches on reservoir performance across multiple objectives. Using the Lièvre River Basin as a case study, we showed that the influence zone approach improved performance indicators compared to the cluster-specific approach across the ensemble of hydroclimatic scenarios. Consequently, the estimation of the adaptive capacity could be refined through the influence zone approach.
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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 | 0.003 | 0.009 |
| 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 | 0.002 |
| 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".