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Influence of Back Support on the Sagittal Plane Biodynamic Response of Seated Human under Vertical Vibration

2014· article· en· W1964657069 on OpenAlexaff
Fan Yang, Subhash Rakheja

Bibliographic record

VenueApplied Mechanics and Materials · 2014
Typearticle
Languageen
FieldMedicine
TopicEffects of Vibration on Health
Canadian institutionsConcordia University
Fundersnot available
KeywordsVibrationUpper bodySagittal planeStructural engineeringWhole body vibrationRandom vibrationEngineeringAcousticsPhysicsAnatomyPhysical medicine and rehabilitationMedicine

Abstract

fetched live from OpenAlex

The dynamic responses of the seated body exposed to vertical vibration are investigated at the two driving-points, formed by the upper body-backrest and buttock-pan interfaces in terms of force-motion relationships. The dynamic interactions of the seated body are characterized in the laboratory in terms of apparent masses at the two driving-points under white noise and track-measured random vertical vibration in the 0.5-40 Hz frequency range. The experiments were performed with 24 adult subjects seated on an idealized rigid seat, representing an automotive seat geometry. The vertical and fore-aft forces at the body-pan and body-backrest interfaces were measured to determine the apparent mass responses at the two driving-points. The results clearly revealed significant dynamic interactions of the upper body with the back support. The results also showed considerable influences of the vibration magnitude, body mass and the subjects’ hands position on the measured biodynamic responses.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.276
Teacher spread0.261 · 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 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

Citations0
Published2014
Admission routes1
Has abstractyes

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