Parametric Assessment of Strategic Buildings for CBRNe and Hybrid Threat Resilience
Bibliographic record
Abstract
This paper presents an innovative method for rapidly assessing building vulnerability, with a focus on potential threats.The approach begins with a historical analysis and a review of state-of-the-art literature obtained from open sources.Subsequently, a tool is introduced, incorporating weighted parameters related to threat typology and available mitigation elements.Critical issues in the overall building vulnerability analysis are pinpointed through a scenario-based approach.While primary literature references are based on explosive attacks (such as Beirut in the '80 s, Nairobi in the '90 s, Oklahoma City in the '90s, etc.), the method also considers non-conventional weapons such as Chemical, Biological, Radiological, and Nuclear (CBRN) threats, along with emerging threats involving direct energy targeting (e.g., Havana Syndrome).The analysis covers six domains: Layout, Structure & Boundary, Technological Plants, In & Out Ways, Cyber, and Building Security Management.Each domain undergoes a comprehensive analysis, identifying threats and developing scenarios and sub-scenarios associated with presumed risks affecting the building.Specific characteristics for each action are identified, with parametric weights assigned to reflect their significance in the overall vulnerability assessment.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".