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Record W1767306909

Whole-body vibration in military vehicles: A literature review

2005· review· en· W1767306909 on OpenAlexaffvenue
Ann Nakashima

Bibliographic record

VenueCanadian acoustics · 2005
Typereview
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsDefence Research and Development Canada
Fundersnot available
KeywordsWhole body vibrationVibrationCognitionComputer scienceEngineeringNoise (video)Effects of sleep deprivation on cognitive performanceSimulationPsychologyAcousticsArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

Military personnel are exposed to high levels of whole-body vibration in armoured vehicles. Since command and control operations are likely to become more mobile in the future, it is of interest to understand the effects of whole-body vibration exposure on human performance and communication. This paper is a review of the effects of whole-body vibration on hearing and cognitive performance. Exposure to vibration has been shown to exacerbate noise-induced hearing loss, which may have implications for radio communication and speech understanding. Vibration does not appear to affect performance for simple cognitive tasks, but it may degrade performance on more complex cognitive tasks, particularly if the exposure is of long duration. This could be of key importance in a command and control situation, in which operators are under high cognitive load. The severity of vibration that is experienced in armoured vehicles makes it difficult to perform realistic experiments in the laboratory, meaning that future studies of its effects on cognitive performance and communication will likely have to be performed in the field.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0050.007
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.042
GPT teacher head0.404
Teacher spread0.362 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations8
Published2005
Admission routes2
Has abstractyes

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