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Record W2782337544 · doi:10.2495/safe-v8-n1-90-97

A comparison between methods for assessment of whole-body vibration exposure: A case study in a limestone quarry

2018· article· en· W2782337544 on OpenAlexvenueno aff
Guido Alfaro Degan, Gianluca Coltrinari, Dario Lippiello, Mario Pinzari

Bibliographic record

VenueInternational Journal of Safety and Security Engineering · 2018
Typearticle
Languageen
FieldMedicine
TopicEffects of Vibration on Health
Canadian institutionsnot available
Fundersnot available
KeywordsWhole body vibrationEnvironmental scienceGeologyVibrationForensic engineeringMining engineeringEngineeringAcousticsPhysics

Abstract

fetched live from OpenAlex

The problem of the whole body vibration (WBV) exposure for operators in the workplace has been analyzed by many researchers that have studied for a long time the complex relationships between this phenomenon and human health risks. Previous studies have shown that drivers of different vehicles, in particular operators in mining activities, are afflicted by serious musculoskeletal injuries due to WBV exposure. Criteria in order to measure and assess equivalent daily exposure, associated with different operations of the vehicles, are referred to standards ISO2631-1, based on method of the frequencyweighted equivalent continuous root mean squared (r.m.s.) acceleration. Numerous studies point out the complexity to outline a clear relationship between WBV exposure and the human health disorders. An experimental campaign was realized in a limestone quarry near Rome and vibration measurements were recorded on four different mining vehicles (dumper, jeep, digger and puncher machine). The vibratory signal was taken at the seat operator/seat interface (X, Y and Z axes) in accordance with the ISO 2631-1 during daily quarry operations. The data obtained were elaborated and finally the results are presented in order to evidence the difference of WBV exposure between the specific operating tasks and some methods for the purpose of exposure dose reduction are suggested.

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)0.002
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 score0.346
Threshold uncertainty score0.373

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.044
GPT teacher head0.465
Teacher spread0.421 · 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

Citations9
Published2018
Admission routes1
Has abstractyes

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