Intraspecific variability in human maxillary bone modeling patterns during ontogeny
Bibliographic record
Abstract
OBJECTIVES: This study compares the ontogenetic bone modeling patterns of the maxilla to the related morphological changes in three human populations to better understand how morphological variability within a species is established during ontogeny at both micro- and macroscopic levels. MATERIALS AND METHODS: The maxillary bones of an ontogenetic sample of 145 subadult and adult individuals from Greenland (Inuit), Western Europe (France, Germany, and Portugal), and South Africa (Khoekhoe and San) were analyzed. Bone formation and resorption were quantified using histological methods to visualize the bone modeling patterns. In parallel, semilandmark geometric morphometric techniques were used on 3D models of the same individuals to capture the morphological changes. Multivariate statistics were applied and shape differences between age groups were visualized through heat maps. RESULTS: The three populations show differences in the degree of shape change acquired during ontogeny, leading to divergences in the developmental trajectories. Only subtle population differences in the bone modeling patterns were found, which were maintained throughout ontogeny. Bone resorption in adults mirrors the pattern found in subadults, but is expressed at lower intensities. DISCUSSION: Our data demonstrate that maxillary morphological differences observed in three geographically distinct human populations are also reflected at the microscopic scale. However, we suggest that these differences are mostly driven by changes in rates and timings of the cellular activities, as only slight discrepancies in the location of bone resorption could be observed. The shared general bone modeling pattern is likely characteristic of all Homo sapiens, and can be observed throughout ontogeny.
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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 | 0.000 |
| 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".