Examining the Impact of Rotated Neck and Trunk Postures on Vertical Seat-to-Head Vibration Transmissibility and Self-Reported Discomfort
Bibliographic record
Abstract
Adopting non-neutral sitting postures while exposed to whole-body vibration (WBV) can put heavy equipment operators at an increased risk for lower back pain and may cause damage to the spinal tissue. A laboratory experiment involving 11 participants (5 females, 6 males) completing four 45-min test sessions incorporating different seated conditions (vibration versus no vibration, and rotation versus no rotation) was used to assess seat-to-head transmissibility (STHT) and self-reported discomfort while in four rotated neck and trunk postures. The vibration exposure profile was a constant vertical sinusoidal signal with a frequency of 3 Hz and 0.7 m/s2 acceleration. Vibration measured at the head was greater than at the seat under all conditions, with a statistically significant effect of time (F1,10 = 101.73, p < 0.001, Eta2 = 0.910) and posture (F3,8 = 5.64, p = 0.023, Eta2 = 0.679). Mean self-reported discomfort ratings revealed increased participant discomfort in rotated neck and trunk positions in both vibration and non-vibration conditions. Increasing time also had a significant (F(1,10) = 15.53, p = 0.003) impact on higher rates of participant discomfort. Overall, it was found that increasing the degree of rotated neck and trunk position from neutral amplified the STHT and self-reported discomfort.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".