Results of close-in effects of enhanced blast weapons, numerical simulation of blast and response of field structures
Bibliographic record
Abstract
The Netherlands research program on `Protection of field structures against the effects of enhanced blast weapons (EBWs)' is strongly related to the Canadian Technology Program on 'Force protection against enhanced biast'. The Netherlands program is divided into four research topics (i) Threat/scenario analysis of EBW (ii) Measurement techniques (iii) Development of prediction models: explosion effects, blast propagation and response of field structures, ammo- and POL-storage (iv) Development of a consequence/risk-analysis tool.This paper presents the development of prediction models for EBW blast and discusses the vulnerability of protective field structures against EBWs threat. Particular in harsh explosion environments the pressure measurements in the fire balt of a thermobaric (TBX) or a fuel air explosion (FAE) are a real challenge. These signals are necessary to develop and validate blast prediction models of EBW explosion effects. Numerical study of the physical effects has led to the implementation of TBX and FAE models into TNO's Blast3D code. The simulation results of the blast propagation show good correspondence to the measured pressures during the Elk Velvet trials at DRDC Suffield, Canada.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".