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Record W1992580881 · doi:10.1504/ijcis.2008.016089

Dynamic recovery of critical infrastructures: real-time temporal coordination

2007· article· en· W1992580881 on OpenAlexaffabout
José R. Martí, Jorge A. Hollman, Carlos E. Ventura, Juri Jatskevich

Bibliographic record

VenueInternational Journal of Critical Infrastructures · 2007
Typearticle
Languageen
FieldEngineering
TopicInfrastructure Resilience and Vulnerability Analysis
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsInterdependencePreparednessNatural disasterGovernment (linguistics)Work (physics)Disaster preparednessDisaster recoveryRisk analysis (engineering)Computer securityComputer scienceEngineeringOperations researchEmergency managementBusinessPolitical science

Abstract

fetched live from OpenAlex

This paper takes a systems engineering approach to the problem of operations coordination among multiple infrastructures to minimise the impact of large disasters on human lives. Temporal coordination is essential to avoid bottlenecks in the simultaneous recovery of multiple infrastructures systems. A solution framework is presented in terms of multiple-delay difference equations which bring out the temporal interdependencies among infrastructures. The present work is part of an effort by the Government of Canada, through the Natural Sciences and Engineering Research Council (NSERC) and Public Safety and Emergency Preparedness Canada (PSEPC) to fund research to develop innovative ways to mitigate large disaster situations.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.004
GPT teacher head0.292
Teacher spread0.287 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations46
Published2007
Admission routes2
Has abstractyes

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