Ontogenetic Allometry and Cranial Shape Diversification Among Human Populations From South America
Bibliographic record
Abstract
Modifications of ontogenetic allometries play an important role in patterning the shape differentiation among populations. This study evaluates the influence of size variation on craniofacial shape disparity among human populations from South America and assesses whether the morphological disparity observed at the interpopulation level resulted from a variable extension of the same ontogenetic allometry, or whether it arose as a result of divergences in the pattern of size-related shape changes. The size and shape of 282 adult and subadult crania were described by geometric morphometric-based techniques. Multivariate regressions were used to evaluate the influence of size on shape differentiation between and within populations, and phylogenetic comparative methods were used to take into account the shared evolutionary history among populations. The phylogenetic generalized least-squares models showed that size accounts for a significant amount of shape variation among populations for the vault and face but not for the base, suggesting that the three modules did not exhibit a uniform response to changes in overall growth. The common slope test indicated that patterns of evolutionary and ontogenetic allometry for the vault and face were similar and characterized by a heightening of the face and a lengthening of the vault with increasing size. The conservation of the same pattern of shape changes with size suggests that differences in the extent of growth contributed to the interpopulation cranial shape variation and that certain directions of morphological change were favored by the trait covariation along ontogeny.
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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".