Context dependent trade-offs in body size among Olympic sports
Bibliographic record
Abstract
There is a context-dependent trade-off in body size of elite runners, such that smaller body sizes are observed among longer distance runners. However, it is unclear if this trade-off in body size is observed in other Olympic sports, such as cycling and swimming. To understand the association between body size and athletic competition, we examined metrics of body size from male and female Olympic athletes competing in swimming, running, and cycling. We collected standard anthropometrics (height, mass, body mass index (BMI), and body surface area) of elite male and female athletes competing in the London 2012 Summer Olympics from a public repository. Anthropometric data were compared between sexes (male and female); between sports (swimming, running, and cycling); and between distances within sports (shorter and longer distance). Males were taller (P<0.001), heavier (P<0.001), and had a larger body mass index (P<0.001) compared to females. Relative to athletes competing in longer distance events, athletes competing in shorter distance were generally taller (running: P<0.001, swimming: P ≤0.014) and heavier (running: P<0.001, swimming: P=0.002 (males) and P=0.148 (females), cycling: P<0.001) for both males and females. Height was not different between shorter and longer distance cyclists. There was a trade-off between body size and distance for each of the three Olympics sports, such that smaller athletes were observed in longer distance events. Although there were large sex-based differences in body size, similar trade-offs in size and event were observed for both males and females. These data suggest that there is an optimal combination of skeletal muscle mass and body size to optimize movement economy that is generally preserved across different modes of human locomotion.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".