Effect of Back Support Condition on Seat to Head Transmissibilities of Seated Occupants under Vertical Vibration
Bibliographic record
Abstract
The seat-to-head transmissibility (STHT) of seated human subjects has been investigated through measurements of vertical seat vibration transmitted to the head in both vertical and fore-and-aft directions. The experiment was performed using 12 adult male subjects exposed to whole-body vertical random vibration in the 0.5–15 Hz frequency range. The effects of back support conditions on the transmitted vibration were investigated by considering three back support conditions (No back support, vertical back support, and inclined back support), and two different hands positions (hands in lap and hands on the steering wheel) while exposed to three magnitudes of excitation (0.25, 0.5 and 1.0 m/s2 rms acceleration). A helmet-strap-mounted accelerometer mounting system was designed to measure the head acceleration motions along the three translational axes. The results attained from ANOVA suggested a strong influence of the back support condition on the magnitude of both the vertical and fore-and-aft STHT. The results also revealed the nonlinear response of the seated body with respect to the excitation magnitude, while the effect of hands position was judged to be insignificant.
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 | 0.000 | 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".