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Record W2519883580 · doi:10.1177/1541931213601214

Comparing the Whole Body Vibration Exposures across Three Truck Seats

2016· article· en· W2519883580 on OpenAlexaff
Fangfang Wang, Hugh Davies, Bronson Du, Peter W. Johnson

Bibliographic record

VenueProceedings of the Human Factors and Ergonomics Society Annual Meeting · 2016
Typearticle
Languageen
FieldMedicine
TopicEffects of Vibration on Health
Canadian institutionsUniversity of WaterlooUniversity of British Columbia
FundersWorkSafe VictoriaWashington State University
KeywordsWhole body vibrationTruckSuspension (topology)AttenuationEnvironmental scienceAir suspensionAutomotive engineeringDirtTransport engineeringVibrationEngineeringStructural engineeringMathematicsAxleAcoustics

Abstract

fetched live from OpenAlex

Studies have shown that there are differences in whole body vibration (WBV) exposures and WBV attenuation performance among different suppliers of air suspension truck seats. With 17 truck drivers operating semi-trucks over two common road types (the same highways and dirt roads), WBV exposures were measured and compared across three different air-suspension truck seats. Similar to a previous study, in the higher-speed, on-road highway conditions, one seat was found to have higher WBV exposures and lower WBV attenuation performance. In off-road conditions at slower speed, there were negligible differences across the three seats. These differences in seat performance have important practical implications. The higher performing seats nearly doubled the amount of time drivers could operate their trucks before reaching the ISO daily vibration action limits from 3 to 6 hours a day to 9 to 11 hours a day. Seat suspension-based design differences are thought to account for the performance differences.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.070
Threshold uncertainty score0.673

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.028
GPT teacher head0.287
Teacher spread0.259 · 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 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

Citations5
Published2016
Admission routes1
Has abstractyes

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