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Record W3118477877 · doi:10.1038/s41598-020-79501-w

Population history and ecology, in addition to climate, influence human stature and body proportions

2021· article· en· W3118477877 on OpenAlexaff
Emma Pomeroy, Jay T. Stock, Jonathan C. K. Wells

Bibliographic record

VenueScientific Reports · 2021
Typearticle
Languageen
FieldMedicine
TopicBirth, Development, and Health
Canadian institutionsWestern University
FundersFP7 Ideas: European Research CouncilNewnham College, University of CambridgeEuropean Commission
KeywordsAnthropometryPopulationDemographyEcologyBiologyGeographySpatial variabilityButterflyPhysical geographyStatisticsMathematics

Abstract

fetched live from OpenAlex

Worldwide variation in human stature and limb proportions is widely accepted to reflect thermal adaptation, but the contribution of population history to this variation is unknown. Furthermore, stature and relative lower limb length (LLL) show substantial plastic responses to environmental stressors, e.g., nutrition, pathogen load, which covary with climate. Thus ecogeographic patterns may go beyond temperature-based selection. We analysed global variation in stature, sitting height and absolute and relative LLL using large worldwide samples of published anthropometric data from adult male (n = 571) and female (n = 268) populations in relation to temperature, humidity, and net primary productivity (NPP). Population history was modeled using spatial eigenvector mapping based on geographic distances reflecting the hypothesized pattern for the spread of modern humans out of Africa. Regression models account for ~ 50% of variation in most morphological variables. Population history explains slightly more variation in stature, sitting height and LLL than the environmental/climatic variables. After adjusting for population history, associations between (usually maximum) temperature and LLL are consistent with Allen's "rule" and may drive similar relationships with stature. NPP is a consistent negative predictor of anthropometry, which may reflect the growth-limiting effects of lower environmental resource accessibility (inversely related to NPP) and/or pathogen load.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score0.300

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.018
GPT teacher head0.303
Teacher spread0.285 · 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 designObservational
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

Citations43
Published2021
Admission routes1
Has abstractyes

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