Toward an Integrated Executable Architecture and M&S Based Analysis for Counter Terrorism and Homeland Security ABSTRACT
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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)
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it