Common evolutionary patterns in the human nasal region across a worldwide sample
Bibliographic record
Abstract
OBJECTIVES: Variation in the external nasal region among human populations has long been proposed in the literature to reflect adaptations to facilitate thermoregulation, air conditioning, and moisture retention in local climates and environments. More specifically, adaptations in populations living in colder climates have often been assumed due to correlational relationships found between variation in the nasal region and climatic variables. Here, we test this hypothesis by applying a quantitative genetics approach based on the Lande model to assess whether variation in the nasal region can be explained by random neutral processes (e.g., genetic drift) or if non-random forces (i.e., adaptation) have contributed significantly to its diversity. MATERIALS AND METHODS: A mixed-sex sample representing 28 population groups from Howells' craniometric dataset were analyzed (n = 2504). Twenty standard measurements were chosen to reflect the external skeletal morphology of the nasal region. We apply statistical tests developed from evolutionary quantitative genetics theory to analyze patterns of within- and between-population divergence under a null hypothesis of genetic drift. RESULTS: This study finds a rejection of genetic drift in all analyses, across tests that involve all 28 populations, exclusively cold-climate populations, and with cold-climate populations excluded, indicating that non-random evolutionary forces have contributed significantly to variation in the nasal region overall. DISCUSSION: These results show that nasal region adaptation is not exclusive to cold-climate populations, which have often been implicated in the literature to drive nasal variation, instead suggesting that the propensity for adaptation in the nasal region is shared among all human populations.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.042 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".