Validation of the Volume Function within Jensen’s (1978) Elliptical Cylinder Model
Bibliographic record
Abstract
An elliptical cylinder model developed by Jensen (1978) has been a widely accepted method for determining human segment inertial parameters. The goal of the present study was to evaluate the accuracy of the segment volume calculation step of this model. Three possible sources of error were examined: between-sex differences in body shape, image ratio, and human inconsistencies in digitizing. Volume estimates for the right lower arm + right hand, right lower leg + right foot, and whole body on 20 young men and women were calculated from digitized images at a ratio of 1:10 and 1:5 of the actual size (measured) and compared to values measured using an underwater displacement technique (criterion). Results showed no differences between the sexes on the accuracy of estimating the three volumes at either image ratio. Combining both sexes, the error in calculating segment volumes with an image-to-actual-size ratio of 1:10 were significantly larger, p < 0.05, than at a ratio of 1:5 for both the lower arm + hand (4.28 ± 2.92% vs. –0.43 ± 2.49%) and the whole body (4.80 ± 2.49% vs. 2.01 ± 2.17%). There was no significant change in mean for the lower leg + foot when the image was increased from 1:10 to 1:5 (–0.12 ± 3.92% vs. –0.81 ± 3.01%, respectively). Although not statistically significant, p > 0.05, a greater magnification seemed to have also reduced the influence of human inconsistencies, which was found to be a primary source of error. When the image-to-actual-size ratio is high (i.e., 1:5) and precaution is taken during digitization, the elliptical cylinder model provides accurate estimates of segment volumes of the whole body and extremities.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".