A Design of Experiments for Statistically Predicting Risk of Adverse Health Effects on Drivers Exposed to Vertical Vibrations
Bibliographic record
Abstract
An injury risk factor (IRF), which indicates the risk of adverse health effect to lumbar rachis arising from mechanical vibrations, is developed. Experiments have been conducted that consider acceleration levels at the seat of drivers, posture, morphology, density, damping rate and body mass as independent variables. A parametric finite-element model of the lumbar rachis has been generated. It is shown that the IRF increases with ageing and an IRF of 30% is proposed as a threshold for fatigue purposes. This level is reached if a peak acceleration level greater than 3 m/s² is applied to a light (55 kg) and an old driver with a low bone density and a damping rate of 20%. This vibration threshold must be reduced to 2.7 m/s² if the driver?s weight increases to 75 kg and to 2 m/s² if the driver is heavy (98 kg).
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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 | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".