MétaCan
Menu
Back to cohort
Record W263277099

DRDC Support to Emergency Management British Columbia's (EMBC) Hazard Risk Vulnerability Analysis (HRVA) and Critical Infrastructure (CI) Programs

2012· article· en· W263277099 on OpenAlexaboutno aff
Lynne Genik, Paul Chouinard

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicInfrastructure Resilience and Vulnerability Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsVulnerability (computing)Vulnerability assessmentRisk managementResilience (materials science)Critical infrastructureRisk analysis (engineering)Critical infrastructure protectionStakeholderHazardRisk assessmentHazard analysisTask (project management)Process managementEngineeringComputer scienceEngineering managementBusinessPsychological resilienceComputer securitySystems engineeringPolitical science
DOInot available

Abstract

fetched live from OpenAlex

Abstract : This paper presents the problem formulation and solution strategy component of the EMBC-DRDC collaborative project agreement for improving EMBC's Hazard Risk Vulnerability Analysis (HRVA) and Critical Infrastructure (CI) Assurance Programs. The methodology is described; the NATO Code of Best Practice for C2 Assessment and a soft operations research approach were applied, along with aspects of capability based planning, systems engineering, and risk management. Preliminary literature searches were performed and are documented here. Stakeholder groups are described and the questions used to elicit their perspectives on the programs and related issues are presented. The result of the analysis was the identification of program requirements, gaps, and proposed projects by DRDC to address aspects of the gaps. The proposed projects include adapting the Major Events Security Framework for use by EMBC, CI assessment tool development through pilot projects, and contracts for a community resilience framework and scenario mission to task templates, among several others.

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 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)0.001
metaresearch head score (Gemma)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.097
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.006
GPT teacher head0.244
Teacher spread0.238 · 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 teacher head, not a consensus.

Study designObservational
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

Citations2
Published2012
Admission routes1
Has abstractyes

Explore more

Same topicInfrastructure Resilience and Vulnerability AnalysisFrench-language works237,207