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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| 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".