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Record W2523223627 · doi:10.4236/wjm.2016.69022

Effect of Mechanical Vibrations on Human Body

2016· article· en· W2523223627 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueWorld Journal of Mechanics · 2016
Typearticle
Languageen
FieldEngineering
TopicStructural Engineering and Vibration Analysis
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsVibrationHuman bodyPhysicsMaterials scienceAcousticsMedicineAnatomy

Abstract

fetched live from OpenAlex

Mechanical vibrations cause forces that affect human bodies. One of the most common positions for human bodies is the seated position. In this work, mathematical models of the seated human body are investigated and simulated in a Simulink/ MATLAB environment. In addition, segments of the human body are studied and models are developed and built by using Simulink/MATLAB. As part of this work, model analysis and state-space methods are used in order to check and validate the results obtained from the simulations. Two types of forces are used to test the whole seated human body under low frequency citation. The first is a sinusoidal wave signal based on literature, and the second is an impulse function. The effects of mechanical vibration on the head and lumbar are studied as these parts of the human body are usually the most effected areas. Kinematic states of the head segment and lumbar are considered. The characteristics of the vibration response on the two segments are also obtained. In addition to the me-chanical vibrations study, this paper is a resource for the development and implementation of models in the Simulink/MATLAB environment.

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.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.836
Threshold uncertainty score0.288

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.009
GPT teacher head0.254
Teacher spread0.244 · 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