Whole Body Vibration Exposure among the Tractor Operator during Soil Tillage Operation: An Evaluation using ISO 2631-5 Standard
Bibliographic record
Abstract
This study is aimed at investigating the effect of various tractor ride conditions on the lumbar spine during rotavator operation. The lumbar spinal response was assessed by determining static compressive dose, i.e., Sed (8). Raw acceleration data were measured on the seat pan and seat backrest to varying tractor velocity, tillage depth, and pulling force. The field experiments have been designed using Taguchi’s L9 orthogonal array and the entire data analyzed in signal-to-noise ratios terms. The mean acceleration responses were dominant along the vertical axis and exposure levels were found beyond the exposure action value, i.e., 0.5 m/s2 as per Directive 2002/44/EU. Mean Sed (8) exceeded the limits of ISO 2631-5 indicating the probability of adverse health effects. Tractor velocity and pulling force have a significant impact on Sed (8) with a 64.43% and 27.73% percentage contribution. The FFT dominant peaks have been found in the 0.8 to 3.7 Hz frequency range. Moreover, the exact frequency of the peaks is found to be dependent on the experimental circumstances. The depicted dominant frequencies found in the range of low frequencies could lead to musculoskeletal disorders since they coincide with the natural frequencies of various parts of the body, especially in the lumbar region.
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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 | 0.001 |
| 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) | 0.001 | 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".