Alternative Metabolic Strategies are Employed by Endurance Runners of Different Body Sizes; Implications for Human Evolution
Bibliographic record
Abstract
Abstract Objective A suite of adaptations facilitating endurance running (ER) evolved within the hominin lineage. This may have improved our ability to reach scavenging sites before competitors, or to hunt prey over long distances. Running economy (RE) is a key determinant of endurance running performance, and depends largely on the magnitude of force required to support body mass. However, numerous environmental factors influence body mass, thereby significantly affecting RE. This study tested the hypothesis that alternative metabolic strategies may have emerged to enable ER in individuals with larger body mass and poor RE. Methods A cohort of male (n = 25) and female (n = 19) ultra-endurance runners completed submaximal and exhaustive treadmill protocols to determine RE, and V̇O2Max. Results Body mass was positively associated with sub-maximal oxygen consumption at both LT1 (maler=0.66,p<0.001; female LT1r=0.23,p=0.177) and LT2 (maler=0.59,p=0.001; femaler=0.23,p=0.183) and also with V̇O2Max(maler=0.60,p=0.001; femaler=0.41,p=0.046). Additionally, sub-maximal oxygen consumption varied positively with V̇O2Maxin both male (LT1r=0.54,p=0.003; LT2r=0.77,p<0.001) and female athletes (LT1r=0.88,p<0.001; LT2r=0.92,p<0.001). Conclusions The results suggest that, while individuals with low mass and good RE can glide economically as they run, larger individuals can compensate for the negative effects their mass has on RE by increasing their capacity to consume oxygen. The elevated energy expenditure of this low-economy high-energy turnover approach to ER may bring costs associated with energy diversion away from other physiological processes, however.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".