Toward an Integrated Executable Architecture and M&S Based Analysis for Counter Terrorism and Homeland Security ABSTRACT
Bibliographic record
Abstract
Over the past few years, defence organizations have begun to shift from Threat –Based Planning to Capability –Based Planning, focusing on a System of Systems construct. Executable Architecture, a Capability Management methodology, provides the means to conduct dynamic analysis of a system, and is emerging as a supporting methodology. By applying the rigor of systems engineering analysis and techniques, and incorporating a holistic blend of people, process and materiel, Executable Architectures can ensure that capabilities are properly designed, efficiently developed, and sustained with a specific focus on interoperability across government departments and defence organizations. Empowered by the use of modeling and simulation to validate the capability requirements and architectures, defence agencies are able to evaluate the potential effectiveness of adding new tools to current capabilities, such as a new sensor to the C4ISR capability. The goal of this study was to test the hypothesis that Executable Architecture provides an effective methodology or framework to address and analyze counter-terrorism and homeland security Capability gaps. This hypothesis was tested in a Homeland Security simulation scenario, where terrorists planted a dirty bomb close to Parliament Hill in Downtown Ottawa. The experiment consisted in conducting an Executable Architecture-based analysis using CORE™, while looking at multiple capability assets such as ground vehicles and an uninhabited aerial vehicle (UAV)
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".