Hierarchical seismic vulnerability assessment of power transmission systems: sensitivity analysis of fragility curves and clustering algorithms
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a sensitivity analysis of the hierarchical decomposition of networks in the context of seismic vulnerability of power systems. The sensitivity analysis is evaluated by varying the fragility curves and clustering algorithms on the vulnerability measure. For the fragility curves, four different curves are used. For sensitivity to clustering, four representative spectral clustering algorithms are used: SM, KVV, NJW, and Meila-Shi algorithms. The resulting vulnerability rankings are compared for a common case study (IEEE-118 test case) in terms of: correlation, identification of critical nodes, and identification of common clusters. The results show the robustness of the methodology with respect to variation of the fragility curves. On the other hand, the selection of the clustering algorithm is found to be a decisive parameter depending on application. If the application requires identification of critical elements a better performance is found.
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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