Data-Informed Risk Analysis of Power Grids: Application of Method for Managing Heterogeneous Datasets
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Power utilities are continuously under high pressure to ensure the best performance of their grid. Nevertheless, power outages continue to be periodically observed. This paper assesses the applicability and implications of the Three-Phases method for optimized dataset selection in dynamic risk analysis, through a case study focusing on vegetation along power lines—a major hazard in power grid management. The case study comprises 17 different real-world datasets originating from 12 different types of data sources. We estimate how these datasets can inform eight parameters related to the physical configuration—one of the three dimensions impacting the probability of tree falls on power lines. The results provide two main take-aways: (1) datasets initially considered as less valuable for risk analysis can end up being the most relevant ones; (2) the potential of knowledge of a dataset needs to be assessed parameter per parameter. The results demonstrate that the Three-Phases method is a step toward traceable, data-driven, and dynamic risk analyses of power grids, resulting in a more reliable management of those large-scale infrastructures.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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