Towards a portable, memory-efficient test system for Conducted Energy Weapons
Bibliographic record
Abstract
We present a readily portable, memory-efficient performance test system (PTS) for Tasers. The proposed PTS has been developed for the most widely used Conducted Energy Weapons (CEW), Taser X26. The PTS is designed in accordance with the CEW Test Procedure, recently adopted and published by a group of experts. This work is an advancement of our earlier work where we developed a performance calibration system for the Taser X26 in 2010. The objective of the proposed PTS is to check whether the electrical specifications of the Taser X26, listed in the CEW test procedure, fall within the manufacturers limits for satisfactory electrical performance or not. Additionally data above and beyond that necessary for performance validation is generated for further study of failure modes and biomedical effects. A new data file format is pro posed to create a consistent structure for a data repository and data mining in future research. The CEW is electrically connected to a calibrated dummy resistive network of 600 ohms and fired for 5 seconds while the output voltage is captured with a sampling rate of 10 MS/s at 12 bit resolution. In comparison with our earlier PTS, this one is faster, of higher resolution and higher accuracy, mobile, and memory-efficient.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".