A mixed model for the relationship between latitude and human post‐cranial form
Bibliographic record
Abstract
The general adherence of modern human body proportions to ecogeographic rules is frequently argued to be the result of thermoregulatory adaptation to climate. However, much of the history of human migrations follows the same clines that are associated with trends in body form. It is therefore important to test hypotheses about human adaptation to climate with approaches that account for population history and structure. In this project, we investigate the relationship between latitude and post‐cranial form in modern humans, with the goal of accounting for population history/structure and providing estimates of effects sizes and error. Using a multivariate quantitative genetics mixed model, we estimate morphological effects associated with latitude for long bone lengths and body size using osteometric data from 121 globally‐distributed populations and geographically matched genetic data representing 28 populations. The model includes a random effect for population structure (genetic relatedness) and a fixed effect for latitude. We found that among‐group variation was tightly correlated between limb lengths and body size measures respectively, but that these trait groups were fairly independent of each other. In addition, only bi‐iliac breadth demonstrates a clear directional effect once population history is taken into consideration, though directional trends skew positive for humeral length and negative for distal limb lengths, supporting previous research. By disentangling latitudinal effects from population structure using a mixed model approach, we add to the growing body of research exploring these strong underlying associations, and allow for a better understanding of the relationship between environmental and post‐cranial morphological diversity. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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 | 0.016 | 0.024 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".