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Record W2132903393 · doi:10.1260/026309206779800470

Multi-Axis Sinusoidal Whole-Body Vibrations: Part I — How Long Should the Vibration and Rest Exposures Be for Reliable Discomfort Measures?

2006· article· en· W2132903393 on OpenAlexafffund
James P. Dickey, Michele Oliver, Paul-Émile Boileau, Tammy Eger, Lana M. Trick, A. Michelle Edwards

Bibliographic record

VenueJournal of low frequency noise, vibration and active control · 2006
Typearticle
Languageen
FieldMedicine
TopicEffects of Vibration on Health
Canadian institutionsLaurentian UniversityInstitut de recherche Robert-Sauvé en santé et en sécurité du travailUniversity of Guelph
FundersWorkplace Safety and Insurance Board
KeywordsVibrationWhole body vibrationRest (music)Structural engineeringAcousticsPhysical medicine and rehabilitationMedicineEngineeringPhysics

Abstract

fetched live from OpenAlex

Laboratory-based whole-body vibration studies often involve complex experimental designs, dozens of vibration exposures and multiple sessions. Shortening the test vibration duration would increase experimental efficiency by permitting more trials in the same time period. This study evaluated reported discomfort based on different sinusoidal vibration durations and amounts of rest between successive vibrations. Ten subjects were exposed to four blocks of vibration trials (15/20 second vibration and 5/10 second rest durations). Each block of 37 trials included repeated single axis, planar, and 6 degree of freedom multi-axial vibrations. These repeated trials were analysed to evaluate whether discomfort varied between the different blocks. We did not observe any statistically significant differences in discomfort between the different vibration and rest durations. This finding is useful for designing future vibration experiments. Part II of this study evaluates the relationship between discomfort and vibration exposure.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.780
Threshold uncertainty score0.939

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.002
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 head0.031
GPT teacher head0.301
Teacher spread0.269 · 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 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

Citations27
Published2006
Admission routes2
Has abstractyes

Explore more

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