Comparison between ISO 2631–1 Comfort Prediction Equations and Self-Reported Comfort Values during Occupational Exposure to Whole-Body Vehicular Vibration
Bibliographic record
Abstract
It is important to understand whole body vibration (WBV) since it affects comfort and is important in worker health and performance. Although discomfort can be subjectively evaluated, the ISO 2631–1 standard predicts discomfort based on vibration magnitudes, frequencies and durations. The objective of this study was to determine whether the ISO 2631–1 prediction method produces similar results to self-reported discomfort levels during routine heavy machinery operations in the field. While working under normal conditions, 6 df seat-pan vibration data were recorded in construction, mining, and forestry vehicles. At 5-minute intervals, operators rated their discomfort based on the preceding minute of vibration exposure. Discomfort was predicted from the vibration total value for each corresponding one-minute vibration profile. Each industry showed consistent trends between the predicted and self-reported discomfort; however, there were different relationships between industries. Construction showed a weak positive relationship ( r 2 =0.09) between predicted and self-reported discomfort values, whereas both forestry and mining showed no relationship. The predicted discomfort levels did not accurately represent self-reported discomfort; this is similar to some previous studies, but contrasts with other studies. This variability may be due to discrepancies with the prediction equations, or perhaps due to additional factors being incorporated into self-reported comfort measures, such as temperature, noise, and fatigue.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".