Anterior diaphyseal curvature of the femur and tibia has biomechanical consequences during unloaded uphill locomotion
Bibliographic record
Abstract
The biomechanics of limb bone curvature are complex, and though anterior curvature clearly exhibits some relationship with behavior, the mechanisms shaping it and its biomechanical purpose remain unclear. Among both Neanderthals and humans, anterior limb bone curvature correlates strongly with other limb bone adaptations known to reflect high mobility, particularly in steep variable terrain. We developed a computational musculoskeletal model to test the impact of opposing patterns of human femoral and tibial anterior curvature on muscle kinetics during a proxy for uphill walking: stair-climbing. Femoral and tibial laser scans were used from two archaeological individuals matched for estimated body mass, bi-iliac breadth, and crural index but with reverse patterns of anterior diaphyseal curvature. A default OpenSim musculoskeletal model was modified twice – once to each individual’s anatomy – and the resulting models performed stair-climbing gait simulations with experimentally-collected motion capture data. Both custom MSk models followed the general gait kinematics expected based on stair-climbing gait studies, but differed in muscle kinetics in key areas. When a curved femur was paired with a straight tibia, the opposite pattern of recruitment of bi-articular superficial hamstring muscles was observed compared to the pairing of a straight femur with a curved tibia. Pairing a curved femur with a straight tibia was also associated with reduced soleal recruitment and an increased reliance on secondary ankle plantarflexors. These effects are attributed to variation in muscle moment arms about the knee and the relationships that curvature likely has with other three-dimensional morphological variation within the bone/limb. While the tissue-level mechanisms shaping anterior limb bone curvature remain poorly understood, results of the current study suggest that opposing patterns of variation in long bone curvature within the leg do elicit different kinetic solutions to the problem of achieving the same gait kinematics during uphill locomotion. This is an important first step not just in better understanding the biomechanical impact of anatomical variation in bone curvature on locomotion, but also contributes more broadly to the recognition in biological anthropology of variability in the relationship between locomotor function and underlying skeletal structure.
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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) | 0.001 | 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".