Intraindividual variation in the cross‐sectional geometry of the first metatarsal, femur, and tibia
Bibliographic record
Abstract
Abstract Objectives The examination of cross‐sectional properties (CSP) of long bones can inform about differential activity patterns and levels of mobility. Typically, these analyses have focused on the femur and tibia, but metatarsals might also be informative. This study examines femora, tibiae, and first metatarsals to evaluate the relationships between long bone CSP within the lower limb and how this might improve our interpretations of metatarsal variability. Materials and Methods The study includes protohistoric Andaman Islanders (n = 26) and Later Stone Age (LSA) Southern Africans (n = 25) from approximately 10,000–500 BP. Skeletal data were acquired from past studies of these groups. Correlations were used to evaluate the relationships between CSP of lower limb bones within individuals. Principal component analyses were used to evaluate how each lower limb bone contributed to variation in CSP among individuals. Results The correlations between the CSP in the femur and tibia were always strong, but the correlations with the first metatarsal are variable. Variation in femoral loading largely drove PCAs, with less influence by the first metatarsal, or loading in the opposite direction. Discussion The femur and tibia experience similar patterns of mechanical loading, specifically in compression and tension, but the first metatarsal does not reflect the same biomechanical patterns as the femur and tibia. Similarly, the first metatarsal often drove variation in the opposite direction as the femur, indicating differences in the mechanical loading pattern between the two bones.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".