NATO Framework for Modeling and Simulation of Human Lethality, Injury, and Impairment From Blast-Related Threats and Its Practical Implementation to Support Blast Injury Research
Bibliographic record
Abstract
Abstract Blast-related trauma was the predominate source of casualties within Iraq and Afghanistan and has maintained in the Ukraine conflict. Computational modeling is anticipated to accelerate discovery of novel solutions for mitigating injuries and reduce costs in research and development. The North Atlantic Treaty Organization (NATO) saw the future value in a comprehensive, whole human blast effects modeling capability to counter emerging blast threats resulting in establishment of a research technical group (RTG) to develop a framework for the capability. The RTG performed a literature review demonstrating the lack of such a capability along with the necessary pieces needed for a framework. RTG development framework consists of: Model 1 Threat characterization: generates a computational representation of the blast-threat; Module 2 Biophysics: produces the relevant loading profile and predicts biomechanical, pathophysiological, and neurological responses; Module X Injury Prediction and Medical Diagnosis: provides predictions on injuries (e.g., fracture) and Module Y Medical Outcomes provides understanding of the clinical consequences (e.g., functional incapacitation) of those injuries. The framework can assist in mitigating blast injuries and their consequences on Service Member readiness. Key hurdles to its development include a lack of high rate material characteristics and siloed model development.
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How this classification was reachedexpand
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 | 0.004 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".