Thrifty phenotype versus cold adaptation: trade-offs in upper limb proportions of Himalayan populations of Nepal
Bibliographic record
Abstract
The multi-stress environment of high altitude has been associated with growth deficits in humans, particularly in zeugopod elements (forearm and lower leg). This is consistent with the thrifty phenotype hypothesis, which has been observed in Andeans, but has yet to be tested in other high-altitude populations. In Himalayan populations, other factors, such as cold stress, may shape limb proportions. The current study investigated whether relative upper limb proportions of Himalayan adults (n = 254) differ between highland and lowland populations, and whether cold adaptation or a thrifty phenotype mechanism may be acting here. Height, weight, humerus length, ulna length, hand length and hand width were measured using standard methods. Relative to height, total upper limb and ulna lengths were significantly shorter in highlanders compared with lowlanders in both sexes, while hand and humerus length were not. Hand width did not significantly differ between populations. These results support the thrifty phenotype hypothesis, as hand and humerus proportions are conserved at the expense of the ulna. The reduction in relative ulna length could be attributed to cold adaptation, but the lack of difference between populations in both hand length and width indicates that cold adaptation is not shaping hand proportions in this case.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".