Fabric armour erosion using a buried charge ejecta analog
Bibliographic record
Abstract
The threat posed by buried explosive charges and their explosive dispersal of environmental debris is associated with a variety of debilitating injuries. The dispersion profile of such threats includes particulate in a broad range of particle length scales, which are more destructive to ballistic fabrics than larger particles alone. Smaller particles are initially dispersed at higher speeds, degrading armour fabrics prior to the arrival of the larger penetrating particulate. In the present work, we present a low-cost industrial abrasion test method to investigate the abrasive wear in neat and polymer-coated ballistic fabrics to determine the level of degradation of the fabrics under the abrasive load. These fabrics were subjected to the impingement of an abrasive jet with average particle velocity of 187 ± 20 m/s. The results showed evidence of significant degradation and failure within the ballistic fabrics, that would certainly influence their subsequent ballistic performance. The addition of polymer coatings was able to reduce the abrasive degradation of the fabrics. The failure modes of the polymer composites are similarly described. This methodology shows promise as a means of armour material screening for this particular threat.
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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 | 0.000 |
| 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) | 0.001 | 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 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".