Comparability of multiple data types from the bering strait region: Cranial and dental metrics and nonmetrics, mtDNA, and Y‐chromosome DNA
Bibliographic record
Abstract
Different data types have previously been shown to have the same microevolutionary patterns in worldwide data sets. However, peopling of the New World studies have shown a difference in migration paths and timings using multiple types of data, spurring research to understand why this is the case. This study was designed to test the degree of similarity in evolutionary patterns by using cranial and dental metric and nonmetric data, along with Y-chromosome DNA and mtDNA. The populations used included Inuits from Alaska, Canada, Siberia, Greenland, and the Aleutian Islands. For comparability, the populations used for the cranial and molecular data were from similar geographic regions or had a shared population history. Distance, R and kinship matrices were generated for use in running Mantel tests, PROTEST analyses, and Procrustes analyses. A clear patterning was seen, with the craniometric data being most highly correlated to the mtDNA data and the cranial nonmetric data being most highly correlated with the Y-chromosome data, while the phenotypic data were also linked. This patterning is suggestive of a possible male or female inheritance, or the correlated data types are affected by the same or similar evolutionary forces. The results of this study indicate cranial traits have some degree of heritability. Moreover, combining data types leads to a richer knowledge of biological affinity. This understanding is important for bioarchaeological contexts, in particular, peopling of the New World studies where focusing on reconciling the results from comparing multiple data types is necessary to move forward.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".