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Record W1972849507 · doi:10.1177/1541931214581386

Initial Biomechanical and performance implications of weapon design

2014· article· en· W1972849507 on OpenAlexaff
Richard Stone, Rob Mayer, Bryce E. Rosenquist

Bibliographic record

VenueProceedings of the Human Factors and Ergonomics Society Annual Meeting · 2014
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Performance
Canadian institutionsLockheed Martin (Canada)
Fundersnot available
KeywordsPreferenceAeronauticsEngineeringLaw enforcementStability (learning theory)Computer securityAction (physics)SimulationComputer scienceLawPolitical science

Abstract

fetched live from OpenAlex

Considerable debate exists among police and military professionals regarding the differences between conventional configuration weapons, where the magazine and action are located ahead of the trigger, and bullpup configuration, where they are located behind the trigger (closer to the user). To date, no published research has attempted to evaluate this question from a physical ergonomics standpoint, and the knowledge that one style might improve stability, reduce fatigue, or result in superior performance is of interest to countless military, law enforcement, and industry experts. In this study, a live-fire evaluation of both weapon styles was performed using a total of 48 participants. Shooting accuracy and fluctuations in biomechanical stability (center of pressure) were monitored while subjects used the weapons to perform standard drills. The bullpup weapon designs were found to provide a significant advantage in both respects, even while subjects showed considerable preference toward the conventional weapons. Although many mechanical and maintenance issues must be considered before committing to either platform, it is clear in terms of basic human stability that the bullpup is the more advantageous configuration.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.859

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.064
GPT teacher head0.357
Teacher spread0.293 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations1
Published2014
Admission routes1
Has abstractyes

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