Defence Against Terrorism Case Study: Simulation-Based Technical Framework to Address Changes in Capability Deficiency
Bibliographic record
Abstract
The events of 9/11, subsequent anthrax incidents, as well as attacks in London, Spain and elsewhere, have brought increasing attention to the probability of NATO nations becoming a target of a terrorist threat. While telecommunications infrastructures may not be a specific target for attack, the consequences of a Chemical, Biological, Radiological, Nuclear, Explosive (CBRNE) incident on such Critical Infrastructures (CI) are debilitating, in part due to interdependencies with other CI. Improving CBRNE emergency preparedness, readiness and response capabilities is critical for both defence and security partners involved in domestic security. This report documents the results of a simulation based – framework for capability assessment of the emergency response Capability to a CBRNE threat at a Telecommunications CI location in Canada. The assessment reviewed the options for address a capability deficiency related to ensuring business continuity of a highly technical environment that would become a contained site after a biological attack, while still requiring maintenance tasks to be performed due to its CI nature. An agile analytical/technical framework methodology associated with Capability options was developed and tested. The assessment captured the as-is emergency response architecture which helped stakeholders understand their role in the incident and displayed the variety of emergency response organizations and levels of government would play a role during
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".