An Overview of Pilot Projects in Support of Critical Infrastructure Resilience
Bibliographic record
Abstract
Abstract : This paper describes two pilot projectsundertaken in the Province of British Columbia (BC) by theDefence Research and Development Canada - Centre forSecurity Science (DRDC CSS) in partnership with EmergencyManagement British Columbia (EMBC) and local communities.The pilot projects occurred between May 2012 and September2013 with three communities of population ranging from 5000 to90,000. Various aspects of CI resilience were targeted, fromunderstanding and analysing dependencies to enhancingplanning. Different analytical approaches were employed andevaluated, including architecture frameworks, soft systemsmethodology and value-focused thinking. In a previous paperdescribing the problem formulation and solution strategy, anumber of challenges to CI resilience were identified, related togovernance, trust, information sharing, culture, assessmentmethodologies and resources. Pilot projects are discussed here inthe context of these challenges. Our experience has led us tohypothesize that it is not tools per se that communities want, butrather meaningful analyses performed with an understanding ofthe local environment.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".