Population Differences in Human Mandibular Growth
Bibliographic record
Abstract
Mandibles are one of the most common bones encountered in the human archaeological record. Variation in mandibular morphology is often associated with differences in subsistence strategy as masticatory stresses influence bone growth and development. Bone growth is stimulated by bone modeling, the process by which formation and resorption occur through the uncoupled activities of osteoblasts and osteoclasts, respectively. There is a limited understanding of bone modeling patterns in humans due to a lack of quantitative data and small sample sizes. The aim of this research was to address the question: is there a shared bone modeling pattern in the mandible of Homo sapiens? To address this question, this research analyzed bone modeling patterns during ontogeny in a sample of 48 mandibles from three geographically diverse populations with differing subsistence strategies: Western Europe (France and Germany), Greenland (Inuit), and South Africa (Khoe khoe and San). The sample was divided into four age categories. Epoxy replicas of the bone were analyzed under a digital microscope, and bone resorption was identified and quantified to create digital bone modeling maps. This study found subtle population differences throughout ontogeny, with bone modeling patterns that diverge during adulthood, possibly related to subsistence strategy. This study contributes to research on bone modeling patterns in the craniofacial system of H. sapiens, expanding on our understanding of bone growth dynamics and morphological adaptations.
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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 | 0.001 |
| 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) | 0.001 | 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".