Population‐specific deviations of global human craniometric variation from a neutral model
Bibliographic record
Abstract
Past studies have revealed that much of human craniometric variation follows a neutral model of population relationships. At the same time, there is evidence for the influence of natural selection in having shaped some global diversity in craniometrics. In order to partition these effects, and to explore other potential population-specific influences, this article analyzes residuals of craniometric distances from a geographically based neutral model of population structure. W.W. Howells' global craniometric data set was used for these analyses, consisting of 57 measurements for 22 populations around the world, excluding Polynesia and Micronesia because of the relatively recent settlement of these regions. Phenotypic and geographic distances were derived between all pairs of populations. Three-dimensional multidimensional scaling configurations were obtained for both distance matrices, and compared using a Procrustes rotation method to show which populations do not fit the geographic model. This analysis revealed three major deviations: the Buriat, Greenland Inuit, and Peru. The deviations of the Buriat and Greenland Inuit appear to be related to long-term adaptation to cold environments. The Peruvian sample is more similar to other New World populations than expected based on geographic distance alone. This deviation likely reflects the evolutionarily recent movement of human populations into South America, such that these populations are further from genetic equilibrium. This same pattern is seen in South American populations in a comparative analysis of classical genetic markers, but not in a comparative analysis of STR loci, perhaps reflecting the higher mutation rate for the latter.
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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 | 0.002 | 0.006 |
| 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 | 0.001 | 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".