Predicting Whole Body Vibration Exposure from Occupational Quad Bike Use in Farmers
Bibliographic record
Abstract
Whole body vibration (WBV) exposure is recognised as a risk factor to the high prevalence of spinal musculoskeletal disorders (MSDs) experienced by farmers. The purpose of this study was to identify self-reported predictors that could be used to develop statistical models for WBV exposure (expressed as A8rms and VDV) in farmers operating agricultural quad bikes. Data were collected in the field from 130 farmers. Linear mixed effects modeling was used to determine the models of best fit. The prediction model for A8rms exposure (explaining 57% of the variance) included farmer age, estimated quad bike driving hours on day of testing and the type of quad bike rear suspension (rigid-axle rear suspension with two shock absorbers). The best model for VDV exposure (explaining 33% of the variance) included farmer age, estimated quad bike driving hours on day of testing and the type of quad bike rear suspension (rigid-axle rear suspension with two shock absorbers). In large epidemiological studies of spinal MSDs, these models would provide an acceptable indication of WBV without the costs of direct measurement.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".