Variation in trabecular bone microarchitecture across rhesus macaque ( <i>Macaca mulatta</i> ) load‐bearing joints
Bibliographic record
Abstract
Globally, human population structure is quickly trending older, increasing the prevalence and systemic burden of age-related skeletal disorders such as osteoporosis. Osteoporosis is characterized by the loss of bone mass, including trabecular bone tissue, leading to skeletal fracture. Although clinically important, fundamental questions remain about normal trabecular bone variation and age-related bone loss. In this study, we use free-ranging rhesus macaques (Macaca mulatta) from the Cayo Santiago Field Station to explore variation in trabecular bone structure. We measured several aspects of trabecular bone structure (maximum and mean bone volume fraction, degree of anisotropy, and trabecular thickness) across the elbow (humerus, ulna, radius), hip (proximal femur), and knee (distal femur, tibia). Analyses of covariance models assessed factors influencing bone structure, including body mass, demography (age, sex, matriline), as well as indices of sociality and early life adversity. Point cloud models of prime and postprime age groups visualized age-related differences in bone structure. We observed significant variation in trabecular bone morphology (max and mean bone volume fraction, degree of anisotropy, and trabecular thickness) across both bones and joints. Sex influenced trabecular thickness, with thicker trabeculae in males. Max and mean bone volume fraction as well as trabecular thickness were positively associated with body mass. Age was associated with significantly lower values of mean bone volume fraction, specifically in the hind limb. We observed significant bone loss specifically in the femoral head and neck. There were no associations of trabecular bone structure with either sociality or early life adversity in this sample. This study provides a comprehensive view of trabecular bone variation by region, sex, mass, and age contextualized by social factors.
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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".