Drifting Diaphyses: Asymmetry in Diametric Growth and Adaptation Along the Humeral and Femoral Length
Bibliographic record
Abstract
This study quantifies regional histomorphological variation along the human humeral and femoral diaphysis in order to gain information on diaphyseal growth and modeling drift patterns. Three thin sections at 40, 50, and 60% bone length were prepared from a modern Mexican skeletal sample with known age and sex to give a longitudinal perspective on the drifting cortex (12 adults and juveniles total, 7 male and 5 female). Point-count techniques were applied across eight cross-sectional regions of interest using the starburst sampling pattern to quantify percent periosteal and endosteal primary lamellar bone at each diaphyseal level. The results of this study show a posterio-medial drift pattern in the humerus with a posterior rotational trend along the diaphysis. In the femur, we observed a consistent lateral to anteriolateral drift and an increase in primary lamellar bone area of both, periosteal and endosteal origin, towards the distal part of the diaphysis. These observations characterize drifting diaphyses in greater detail, raising important questions about how to resolve microscopic and macroscopic cross-sectional analysis towards a more complete understanding of bone growth and mechanical adaptation. Accounting for modeling drift has the potential to positively impact age and physical activity estimation, and explain some of the significant regional variation in bone histomorphology seen within (and between) bone cross-sections due to differing ages of tissue formation. More study is necessary, however, to discern between possible drift scenarios and characterize populational variation.
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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".