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Record W2808908342 · doi:10.1177/1461348418780017

Gender and anthropometric effects on whole-body vibration power absorption of the seated body

2018· article· en· W2808908342 on OpenAlexaff
K.N. Dewangan, Subhash Rakheja

Bibliographic record

VenueJournal of low frequency noise, vibration and active control · 2018
Typearticle
Languageen
FieldMedicine
TopicEffects of Vibration on Health
Canadian institutionsInstitut de recherche Robert-Sauvé en santé et en sécurité du travailConcordia University
Fundersnot available
KeywordsAnthropometryLean body massBody weightBody mass indexAnimal scienceDemographyMedicineBiologyInternal medicine

Abstract

fetched live from OpenAlex

The gender and anthropometric effects on vibration absorbed power characteristics of the seated body are investigated through measurements with 31 males and 27 females considering two different back support conditions, and three levels of vertical vibration (0.25, 0.50, and 0.75 m/s 2 rms acceleration) in the 0.5–20 Hz frequency range. The absorbed power responses for the males and females revealed strong gender effect, which could be mostly related to differences in body mass of the two groups. Subsequent analyses were conducted considering different datasets grouped corresponding to three ranges of the body mass-, build-, and stature-related parameters for both the males and females. Notable differences were evident in the absorbed power responses of the males and females with comparable anthropometric dimensions. Males revealed significantly higher peak and total absorbed power responses compared to the females of comparable anthropometric dimensions, except for the lean body mass. The differences, however, were relatively small in the data for males and females of comparable body mass. The peak power for the females, invariably, occurred at a lower frequency than that for the males. The total absorbed power responses revealed some degree of correlations with the body mass, lean body mass, body fat, and hip circumference ( r 2 >0.60), irrespective of the back support condition and excitation magnitude for both the genders.

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)0.000
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: Empirical
Teacher disagreement score0.699
Threshold uncertainty score0.547

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.010
GPT teacher head0.287
Teacher spread0.277 · 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

Citations17
Published2018
Admission routes1
Has abstractyes

Explore more

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