Eco‐geographic adaptations in the human ribcage throughout a 3D geometric morphometric approach
Bibliographic record
Abstract
OBJECTIVES: According to eco-geographic rules, humans from high latitude areas present larger and wider trunks than their low-latitude areas counterparts. This issue has been traditionally addressed on the pelvis but information on the thorax is largely lacking. We test whether ribcages are larger in individuals inhabiting high latitudes than in those from low latitudes and explored the correlation of rib size with latitude. We also test whether a common morphological pattern is exhibited in the thorax of different cold-adapted populations, contributing to their hypothetical widening of the trunk. MATERIALS AND METHODS: We used 3D geometric morphometrics to quantify rib morphology of three hypothetically cold-adapted populations, viz. Greenland (11 individuals), Alaskan Inuit (8 individuals) and people from Tierra del Fuego (8 individuals), in a comparative framework with European (Spain, Portugal and Austria; 24 individuals) and African populations (South African and sub-Saharan African; 20 individuals). RESULTS: Populations inhabiting high latitudes present longer ribs than individuals inhabiting areas closer to the equator, but a correlation (p < 0.05) between costal size and latitude is only found in ribs 7-11. Regarding shape, the only cold adapted population that was different from the non-cold-adapted populations were the Greenland Inuit, who presented ribs with less curvature and torsion. CONCLUSIONS: Size results from the lower ribcage are consistent with the hypothesis of larger trunks in cold-adapted populations. The fact that only Greenland Inuit present a differential morphological pattern, linked to a widening of their ribcage, could be caused by differences in latitude. However, other factors such as genetic drift or specific cultural adaptations cannot be excluded and should be tested in future studies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".