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Record W2594888378 · doi:10.3233/oer-160242

Human Vibration

2016· article· en· W2594888378 on OpenAlexaboutno aff

Bibliographic record

VenueOccupational Ergonomics · 2016
Typearticle
Languageen
FieldPsychology
TopicErgonomics and Musculoskeletal Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsVibrationComputer scienceStructural engineeringEngineeringAcousticsPhysics

Abstract

fetched live from OpenAlex

Human VibrationVibration enters the body at points in contact with a vibrating surface including the hands, feet, or buttocks and back in the case of a seated person.Much of the vibration entering the body does not cause ill effects, however, depending on the frequency content and entry point, some vibrations may be at the resonant frequency for a specific body portion.In this case, the vibration is considered to be harmful and may lead to injuries such as vibration white finger in the case of vibrations entering the hands or vibration white toe in the case of vibrations entering the feet.Health effects resulting from whole-body vibration (WBV) in which the primary entry points are the buttocks and back can include a myriad of ailments including low-back pain, spinal degeneration, neck pain and headaches to name a few.The conference in Guelph, Ontario, Canada from June 10-13 in 2014 entitled the "5th American Conference on Human Vibration" dealt with precisely these topics and we invited three of the contributors to expand upon their papers and contribute to this Special Issue on Human Vibration (DeShaw and Rahmatalla, Welcome et al. and Goggins et al.).To round out the Special Issue, and to highlight the diversity of human vibration research, a further two papers were solicited (Oliver et al. and Leduc et al.).The papers are presented in this Special Issue in a logical progression according to the topical content.The first paper by DeShaw and Rahmatalla investigated the effects of various back supports on head motion, discomfort and vibration transmissibility when participants were exposed to multiple axis WBV in a laboratory environment.Their results indicated that the lumbar supports combined the advantages of lower head motion and lower discomfort than a flat backrest by helping keep the natural lumbar spine shape thus helping to minimise the adoption of awkward trunk postures.The second paper by Oliver et al. represented the final phase of a three phase study designed to inform companies how to retrofit heavy mobile machines with seats that were more likely to minimise WBV exposure.While WBV levels were reduced, the exposure risk was not completely eliminated thus highlighting the fact that seat designers should be continuing to alter their designs to improve WBV attenuation.The third paper by Welcome et al. provides a comprehensive look at the effectiveness of vibrationreducing (AV) gloves designed to minimise hand transmitted vibration.Similar to suggestions made by Oliver et al. that seats need to be matched to attenuate the specific vibrations machine operators will be exposed to, Welcome et al. concluded that better matching between specific gloves and tools is required to provide optimal protection.The fourth paper by Leduc et al. investigated health and safety training received by construction industry workers regarding hand arm vibration syndrome (HAVS) and the use of AV gloves.Few participants had received HAVS or AV glove specific training, however, at a two month follow-up visit, they observed a more than ten-fold increase in self-reported AV glove use thus highlighting the importance of education and training.The fifth and final paper by Goggins et al. provides one of the first reports in the literature concerning foot-transmitted vibration (FTV).The paper provided the first quantification of frequencies at which resonance occurs in the metatarsal and ankle.By knowing the resonant frequencies, future FTV mitigation strategies can be focused on reducing transmission of those specific vibration frequencies.

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)0.004
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: none
Teacher disagreement score0.275
Threshold uncertainty score0.921

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0050.003
Open science0.0020.003
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.2750.149

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 head0.025
GPT teacher head0.320
Teacher spread0.295 · 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
Published2016
Admission routes1
Has abstractyes

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