Predictors of whole-body vibration exposure experienced by highway transport truck operators
Bibliographic record
Abstract
Whole-body-vibration (WBV) exposure levels experienced by transport truck operators were investigated to determine whether operator's exposure exceeded the 1997 International Standards Organization (ISO) 2631-1 WBV guidelines. A second purpose of the study was to determine which truck characteristics predicted the levels of WBV exposures experienced. The predictor variables selected based on previous literature and our transportation consultant group included road condition, truck type, driver experience, truck mileage and seat type. Tests were conducted on four major highways with 5 min random samples taken every 30 min of travel at speeds greater than or equal to 80 km/h (i.e. highway driving). Results indicated operators were not on average at increased risk of adverse health effects from daily exposures when compared to the ISO WBV guidelines. Significant regression models predicting the frequency-weighted RMS accelerations for the x (F((5,97)) = 8.63, p < 0.01), y (F((5,97)) = 7.74, p < 0.01), z (F((5,61)) = 9.83, p < 0.01) axes and the vector sum of the orthogonal axes (F((5,61)) = 13.89, p < 0.01) were observed. Road condition was a significant predictor (p < 0.01) of the frequency-weighted RMS accelerations for all three axes and the vector sum of the axes, as was truck type (p < 0.01) for the z-axis and vector sum. Future research should explore the effects of seasonal driving, larger vehicle age differences, greater variety of seating and suspension systems and team driving situations.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".