Adapting RIACT for Ice Storm Resilience of the Hydro-Québec Grid: A City-Level Approach
Bibliographic record
Abstract
This paper presents an adaptation and application of the Risk-Informed Asset-Centric (RIACT) process to analyze the resilience of a portion of Hydro-Québec's electric power grid against extreme ice storm risks, when supplying the densely populated Greater Montreal area, one of the power system’s major load centers. The key aspect consists in avoiding widespread or major blackouts and maintaining the functional performance of the area’s critical and essential services. The study identifies critical risks and asset exposures, analyzes potential solutions. The evaluation is aimed at deploying, on an urban grid, measures based on the PERA resilience stages: i) Preparation; ii) Endurance (Absorption); iii) Recovery; and iv) Adaptation. The proposed PERA plan aims to provide insights in implementing effective preventive measures while emphasizing the importance of monitoring, communication, and reporting throughout the process. By integrating resilience and asset management practices, this approach provides a simplified framework for managing risks associated with extreme weather events, ensuring the continuity of essential services.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".