Gender and anthropometric effects on whole-body vibration power absorption of the seated body
Bibliographic record
Abstract
The gender and anthropometric effects on vibration absorbed power characteristics of the seated body are investigated through measurements with 31 males and 27 females considering two different back support conditions, and three levels of vertical vibration (0.25, 0.50, and 0.75 m/s 2 rms acceleration) in the 0.5–20 Hz frequency range. The absorbed power responses for the males and females revealed strong gender effect, which could be mostly related to differences in body mass of the two groups. Subsequent analyses were conducted considering different datasets grouped corresponding to three ranges of the body mass-, build-, and stature-related parameters for both the males and females. Notable differences were evident in the absorbed power responses of the males and females with comparable anthropometric dimensions. Males revealed significantly higher peak and total absorbed power responses compared to the females of comparable anthropometric dimensions, except for the lean body mass. The differences, however, were relatively small in the data for males and females of comparable body mass. The peak power for the females, invariably, occurred at a lower frequency than that for the males. The total absorbed power responses revealed some degree of correlations with the body mass, lean body mass, body fat, and hip circumference ( r 2 >0.60), irrespective of the back support condition and excitation magnitude for both the genders.
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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 | 0.000 | 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) | 0.003 | 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".