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Record W1525215018 · doi:10.1002/9780470087923.hhs243

Managing Critical Infrastructure Interdependencies: The Ontario Approach

2008· other· en· W1525215018 on OpenAlexaffabout
Bruce D. Nelson

Bibliographic record

VenueWiley Handbook of Science and Technology for Homeland Security · 2008
Typeother
Languageen
FieldEngineering
TopicInfrastructure Resilience and Vulnerability Analysis
Canadian institutionsMinistry of Community Safety and Correctional Services
Fundersnot available
KeywordsInterdependenceCritical infrastructureWork (physics)Process (computing)BusinessGovernment (linguistics)Information sharingPrivate sectorFunction (biology)Process managementComputer scienceComputer securityEngineeringEconomic growthPolitical science

Abstract

fetched live from OpenAlex

Abstract The Province of Ontario developed the Critical Infrastructure Assurance Program based upon risk management, business continuity process, and the collaboration of three levels of government and the private sector. It addresses the resiliency of systems and networks and their ability to function at some level throughout a threat. It is an all‐hazards approach that concentrates its efforts in the prevention and mitigation pillars of emergency management. The systems are addressed by sectors and their information sharing networks are an integral part of the program. The program includes a modeling program that is fed by the work of the sectors and addresses the strength of relationships amongst the sectors’ dependencies and interdependencies. The program's work is validated through an annual interdependency exercise involving all sectors and smaller exercises on particular threats or identified vulnerabilities between sectors. The most valuable part of the program is the sharing of knowledge throught the information‐sharing network.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.090
Threshold uncertainty score0.367

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.004
Science and technology studies0.0070.004
Scholarly communication0.0050.003
Open science0.0020.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.211
Teacher spread0.207 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2008
Admission routes2
Has abstractyes

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Same venueWiley Handbook of Science and Technology for Homeland SecuritySame topicInfrastructure Resilience and Vulnerability AnalysisFrench-language works237,207