Damage characteristics of fabrics created by <scp>TASER</scp> probes
Bibliographic record
Abstract
TASER® weapons are conducted energy weapons (CEWs) that are frequently used by police departments around the world. CEWs can be deployed in two methods: drive stun application and probe deployment. This study aims to examine damages caused by TASER devices on fabrics and whether types of fabric material and TASER models could contribute to different damage features. Three types of white fabric were used, including 100% cotton, 100% polyester, and 65:35 polyester-cotton blend. Three models: TASER X26P, TASER X2, and TASER 7 were shot onto each type of fabric, with five repetitions each. Each damaged area on the fabric caused by a probe is a sample (n = 90) and was examined with a Keyence digital microscope. Images were captured by the Keyence microscope and measurements were recorded, including damage dimensions, fabric condition, evidence of burning, and extra findings. The presence of fused yarn ends was found to be statistically significant across the fabric types, and no damage features were found that may assist in the identification of TASER models. Other damage features including damage dimensions, discoloration, and fiber deformation were not found to be showing apparent differences according to statistical analysis. The conclusions made by this research should be used with caution due to the small sample size.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".