Test and injury assessment methods for non-lethal kinetic energy projectiles:
Bibliographic record
Abstract
As is the case for all armed forces, the Netherlands’ MoD requires new weapons and munitions to be qualified according to existing rules and regulations. However, to date no generally accepted qualification process for Non-Lethal Weapons (NLW) exists, and suitable test and assessment protocols for different types of NLW are needed. Even for the widely available class of non-lethal kinetic energy projectiles objective ways of qualification are lacking. So, we developed test protocols on injury risk assessment for impacts on the head, thorax, abdomen and skin by non-lethal kinetic energy projectiles. Such protocols should contain a scientifically sound and reproducible way of testing by means of a suitable model (surrogate), a method of testing, and meaningful criteria (e.g. for injury potential). Based on literature study as well as experimental work, test and assessment protocols are drawn up. The results were discussed in several international gatherings. For the skin, head and thorax there are surrogates available as well as injury predictors. The head procedure is a modification of the Canadian ballistic helmet standard. A certification procedure for the dynamic deflection of the skin material on the head surrogate is still required, as well as specification of the resonance frequency of the full system and consequently data conditioning. For the skin test procedures developed by Wayne State University (WSU) are adopted. The skin method needs to be studied in more detail, e.g. by performing round robin tests. The proposed thorax procedure is based on research at WSU. At present, two thorax surrogates, 3-RBID (3 Rib Ballistic Impact Dummy) and BTTR (Blunt Trauma Torso Rig) with equal potential are available. However, production versions are still under development, and final certification procedures, biofidelity validation etc. are not yet defined. Therefore, a choice for one thorax surrogate over the other is not made yet. So far, there is no biofidelic and certified surrogate of the abdominal area that can measure the injury predictors for the specific loading regime which is seen for less-lethal kinetic energy projectiles. For the time being, a criterion that predicts injury due to projectile impact based on projectile parameters is proposed.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".