Un espace de résilience dédié à la dépendance à l’électricité des infrastructures critiques municipales
Bibliographic record
Abstract
This article addresses the growing vulnerability of municipal critical infrastructures to their dependence on electricity, a situation exacerbated by complex interdependencies. Major power outages, considered systemic risks, are difficult to anticipate and control. Moreover, within a given territory, such outages affect a wide range of infrastructures simultaneously. Consequently, consequence management requires collaborative and adaptive governance among all relevant stakeholders to mitigate impacts on populations. In this context, the concept of a resilience space is introduced. It is defined as a structured framework bringing together municipal actors and the power grid operator to strengthen both individual and collective resilience through enhanced cooperation. The central tool is the Common Situational Picture, which maps infrastructures’ response capacities and vulnerabilities, thereby supporting shared understanding and the development of adapted strategies. The implementation of the resilience space in the Montréal region has demonstrated significant benefits: improved identification of vulnerable sectors, adaptation of municipal emergency plans, and strengthened relationships among all involved stakeholders. The sustainability of this approach relies on clear governance, secure information sharing, and neutral leadership. It is becoming increasingly critical in the face of emerging challenges related to the energy transition and climate change.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".