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Record W606938058 · doi:10.3233/oer-2006-63-401

Whole-body vibration exposure experienced by mining equipment operators

2007· article· en· W606938058 on OpenAlexaffabout
Tammy Eger, Alan W. Salmoni, Adam P. Cann, Robert J. Jack

Bibliographic record

VenueOccupational Ergonomics · 2007
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsWestern UniversityLaurentian University
Fundersnot available
KeywordsWhole body vibrationAccelerometerLoaderTruckAutomotive engineeringVibrationEngineeringTractorHaulageLift (data mining)Structural engineeringMechanical engineeringComputer scienceAcoustics

Abstract

fetched live from OpenAlex

Whole-body vibration exposure levels were measured during the operation of fifteen different types of mobile mining equipment commonly used in Ontario mines. A tri-axial seat pad accelerometer was used to measure vibration exposure when the mining vehicle was operated from a seated position and a tri-axial accelerometer secured to floor, between the operator's feet, was used to measure vibration exposure when the mining equipment was operated from a standing position. Measurements were conducted in accordance with the procedures described in the 1997 ISO 2631-1 standard. Determination of likely health risks for equipment operators were based on a comparison of the measured vibration exposure levels with Health Guidance Caution Zone limits presented in Annex B of the ISO 2631-1 standard. Six vehicles (UG haulage truck, bulldozer, 3.5 yard LHD, cavo loader, muck machine, and personnel carrying tractor) were above the Health Guidance Caution Zone limit, assuming an eight hour exposure period while four vehicles (grader, 7 yard LHD, scissor lift truck and locomotive) were within the Health Guidance Caution Zone limit.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.063

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 head0.058
GPT teacher head0.435
Teacher spread0.378 · 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

Citations77
Published2007
Admission routes2
Has abstractyes

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