Simulation and anticipation of domino effects among critical infrastructures
Bibliographic record
Abstract
Interdependencies among critical infrastructures (CIs) are the cause of domino effects that may have serious consequences for society. To limit the consequences of these phenomena, it is important to be able to anticipate any situation that may trigger a domino effect. To do so, it is necessary to have a good understanding of how CIs are interlinked and how they rely on each other to properly operate. Once this achieved, a system must be put in place in order to model the possible propagation of domino effects and to alert the right people at the right time in order for them to take proper action. This paper presents a prototype of an early warning system (EWS) designed to anticipate and model the propagation of failures among CIs. Called DOMINO, this system makes it possible to rapidly visualise, in time and space, the propagation of a domino effect and to promptly identify the critical infrastructures potentially impacted. Along with effective communication systems, such tool can facilitate the exchange of relevant information between infrastructures operators and managers enabling them to put in place mitigation measures and to limit the consequences of domino effects.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".