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Record W2809404423 · doi:10.1098/rsos.172174

Thrifty phenotype versus cold adaptation: trade-offs in upper limb proportions of Himalayan populations of Nepal

2018· article· en· W2809404423 on OpenAlexaff
Stephanie Payne, Rajendra Kumar BC, Emma Pomeroy, Alison Macintosh, Jay T. Stock

Bibliographic record

VenueRoyal Society Open Science · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHigh Altitude and Hypoxia
Canadian institutionsWestern University
FundersSigma XiaNational Geographic Society
KeywordsAdaptation (eye)BiologyEvolutionary biologyPhenotypeGeographyGeneGeneticsNeuroscience

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.556
Threshold uncertainty score0.281

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.039
GPT teacher head0.319
Teacher spread0.280 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations21
Published2018
Admission routes1
Has abstractyes

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