DRDC Support to Emergency Management British Columbia's (EMBC) Hazard Risk Vulnerability Analysis (HRVA) and Critical Infrastructure (CI) Programs
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".