Risk assessment of the whole-body vibration exposure for drivers of armored vehicles: A case study
Bibliographic record
Abstract
It is widely recognized in the field of safety at workplace that professional exposure to whole-body vibration (WBV) may generate unfavorable effects on workers' health.Among many involved categories, professional drivers are clearly one of the most exposed groups, as exposure time may last for the whole working period.This research is based on the results of measurements gathered from 14 subjects who drove vehicles for urban use.In particular, in order to highlight the effects of vehicle armoring on professional WBV dose, two sampling campaigns were carried out.In the first case, a car for standard use was used while, in the second one, another vehicle of the same model was modified with the installation of the armor-plate for ballistic protection.The assessment was carried out in accordance with ISO 2631-1(97), under the same boundary conditions, and finally the daily exposure parameter was assessed.Furthermore, to allow a comparison independent of individual factors, the exposed subjects were divided into homogeneous groups of different classes based on their body weight and height.The results obtained showed that WBV exposure is clearly connected with vehicle characteristics.In particular, the installation of bulletproof armor, contributing to a change in the car mass distribution and its total weight, determines a generalized reduction of professional dose.This reduction may be quantified in a range from 10% to 20% depending on the individual characteristics of the driver.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".