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Record W1978005845 · doi:10.1504/ijcis.2008.020156

The operational tools for managing physical interdependencies among critical infrastructures

2008· article· en· W1978005845 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
KeywordsInterdependenceDomino effectRisk analysis (engineering)Computer scienceCritical infrastructureSet (abstract data type)Order (exchange)Computer securityEngineeringBusiness

Abstract

fetched live from OpenAlex

As a result of advances in information technology and the necessity of improved efficiency, Critical Infrastructures (CIs) have become increasingly automated and interlinked over the years. This linkage between CI results in a very complex and dynamic system. However, this growing complexity of CI and their interdependencies reveals new vulnerabilities. In fact, the interdependencies between CI are a true means of the propagation of hazards from one network to another. Understanding these interdependencies is necessary to prevent any cascading effects to affect the functioning of these infrastructures. This paper presents a concrete set of tools enabling the management of physical interdependencies among the CI. Based on the resources exchanged by CI, these tools (consequence curves and flexible cartographic representations) allow the visualisation of the evolution of domino effects in time and space, giving the CI managers the potential 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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.317
Threshold uncertainty score0.858

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.301
Teacher spread0.288 · 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