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Record W2002718374 · doi:10.1109/memea.2010.5480210

Development of a performance calibration system for X-26 tasers

2010· article· en· W2002718374 on OpenAlexaff
David M. Dawson, Yasheng Maimaitijiang, Andy Adler

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicRestraint-Related Deaths
Canadian institutionsCarleton University
Fundersnot available
KeywordsCalibrationResistive touchscreenOhmVoltageWork (physics)Test (biology)Weapon systemMeasure (data warehouse)Computer scienceReliability engineeringElectrical engineeringComputer securityEngineeringSimulationMechanical engineeringStatisticsPhysics

Abstract

fetched live from OpenAlex

Conducted Energy Weapons (specifically the Taser) are being increasingly used by police in several countries, and have also been subject to significant media concern over the level of emissions and applicable safety standards. One issue has been the variability in electrical output between weapons, and of individual weapons over time. In order to address this issue, we present work to: 1) establish consensus on the appropriate electrical parameters to characterize a weapon's biomedical effects, and 2) the development and design of a portable test system to measure these parameters. A weapon is electrically connected to a calibrated dummy resistive load of 600 ohms and fired for 5s while the output voltage is measured and the parameters are subsequently calculated. This test system has been used to characterize 256 shots from 84 weapons over 8 test episodes spanning 16 months.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.003

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.

Opus teacher head0.015
GPT teacher head0.257
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2010
Admission routes1
Has abstractyes

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