Energetics as a driver of human morphological thermal adaptation; evidence from female ultra-endurance athletes
Bibliographic record
Abstract
Functional benefits of the morphologies described by Bergmann's and Allen's rules in human males have recently been reported. However, the functional implications of ecogeographical patterning in females remain poorly understood. Here, we report the findings of preliminary work analysing the association between body shape and performance in female ultramarathon runners (n = 36) competing in hot and cold environments. The body shapes differed between finishers of hot and cold races, and also between hot race finishers and non-finishers. Variability in race performance across different settings supports the notion that human phenotype is adapted to different thermal environments as ecogeographical patterns have reported previously. This report provides support for the recent hypothesis that the heightened thermal strain associated with prolonged physical activity in hot/cold environments may have driven the emergence of thermally adaptive phenotypes in our evolutionary past. These results also tentatively suggest that the relationship between morphology and performance may be stronger in female vs. male athletes. This potential sex difference is discussed with reference to the evolved unique energetic context of human female reproduction. Further work, with a larger sample size, is required to investigate the observed potential sex differences in the strength of the relationship between phenotype and performance.
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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 | 0.000 | 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) | 0.002 | 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".