3D fascicle orientations in triceps surae
Bibliographic record
Abstract
The aim of this study was to determine the three-dimensional (3D) muscle fascicle architecture in human triceps surae muscles at different contraction levels and muscle lengths. Six male subjects were tested for three contraction levels (0, 30, and 60% of maximal voluntary contraction) and four ankle angles (-15, 0, 15, and 30° of plantar flexion), and the muscles were imaged with B-mode ultrasound coupled to 3D position sensors. 3D fascicle orientations were represented in terms of pennation angle relative to the major axis of the muscle and azimuthal angle (a new architectural parameter introduced in this study representing the radial angle around the major axis). 3D orientations of the fascicles, and the sheets along which they lie, were regionalized in all the three muscles (medial and lateral gastrocnemius and the soleus) and changed significantly with contraction level and ankle angle. Changes in the azimuthal angle were of similar magnitude to the changes in pennation angle. The 3D information was used for an error analysis to determine the errors in predictions of pennation that would occur in purely two-dimensional studies. A comparison was made for assessing pennation in the same plane for different contraction levels, or for adjusting the scanning plane orientation for different contractions: there was no significant difference between the two simulated scanning conditions for the gastrocnemii; however, a significant difference of 4.5° was obtained for the soleus. Correct probe orientation is thus more critical during estimations of pennation for the soleus than the gastrocnemii due to its more complex fascicle arrangement.
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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 | 0.001 | 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) | 0.001 | 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".