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Record W2013783032 · doi:10.1504/ijcis.2008.020158

Modelling interdependencies among critical infrastructures

2008· article· en· W2013783032 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Critical Infrastructures · 2008
Typearticle
Languageen
FieldEngineering
TopicInfrastructure Resilience and Vulnerability Analysis
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsInterdependenceCascading failureDomino effectCritical infrastructureAnticipation (artificial intelligence)Risk analysis (engineering)Computer scienceVulnerability (computing)Computer securityInterdependent networksComplex networkEngineeringBusinessElectric power systemArtificial intelligence

Abstract

fetched live from OpenAlex

Over the years, Critical Infrastructures (CIs) have become increasingly automated and interlinked. This linkage between CIs results in a very complex and dynamic system which increases their vulnerability to failures. In fact, interdependencies between CIs are a true means of propagation of hazards from one network to another. Thus, when an infrastructure is experiencing difficulties and failures, it can rapidly generate a cascading effect affecting the other infrastructures. Identifying, understanding and modelling these interdependencies is thus necessary to prevent these cascading effects. This paper presents a model developed to understand the interdependencies between CIs and to prevent cascading effects from happening. Based on the resources exchanged by CIs, this model allows the visualisation and the anticipation of domino effects in time and space, allowing CI managers to set up convenient preventive and protective measures in order to avoid their propagation.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.453
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.013
GPT teacher head0.274
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it