Muscle force interacts with stature to influence functionally related polar second moments of area in the lower limb among adult women
Bibliographic record
Abstract
OBJECTIVES: We sought to determine the relationships between muscle size, function, and polar second moments of area (J) at the midshaft femur, proximal tibia, and midshaft tibia. MATERIALS AND METHODS: We used peripheral quantitative computed tomography to quantify right femoral and tibial J and soft tissue cross-sectional areas, and force plate mechanography to quantify peak power output and maximum force of the right limb, among athletic women and control subjects. RESULTS: Lower limb bone J exhibited strong relationships with estimated force but not power between both groups. Among controls, the strongest relationships between force and J were found at the midshaft femur. Among athletes, these relationships shifted to the tibia, regardless of body size, likely reflecting functional strain related to the major knee extensors and ankle plantarflexors. Together, muscle force and stature explained as much as 82 and 48% of the variance in lower limb bone J among controls and athletes, respectively. DISCUSSION: Results highlight the importance of considering relevant muscle function variables (e.g., force and lever arm lengths) when interpreting behavioral signatures from skeletal remains. Future work to improve the estimation of muscle force from skeletal remains, and incorporate it with lever arm length into analyses, is warranted. Results also suggest that, in doing so, functional relationships between a given section location and musculature should be considered.
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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 | 0.002 |
| 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) | 0.002 | 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".