Survival of the Fastest: The Multivariate Optimization of Performance Phenotypes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
INTRODUCTION: Trade-offs are widespread in biological systems. Any investment in one trait must necessarily limit the investment in other traits. Still, many studies of physiological performance produce positive correlations between traits that are expected to trade-off with one another. Here we investigate why predicted trade-offs may often go unmeasured in studies of human athletes. METHODS: Triathletes compete in consecutive swimming, cycling, and running events as a single competition, events whose physical demands may be especially prone to generating performance trade-offs. Performance variation in these three events interacts to explain overall variation in athletic performance. RESULTS: We show that individual variation in athletic performance can mask trade-offs among disciplines, giving the impression that high-performance triathletes are athletic generalists. Covariance in race performance across the three disciplines was positive in the most elite athletes but became increasingly negative as race times increased. CONCLUSIONS: These performance trade-offs among the disciplines preclude the realization of a generalist athlete except in the most elite triathletes, a result similar to the "big houses, big cars" phenomenon in life history evolution. This distinction between trait combinations that are favored for optimal performance versus constrained by trade-offs was only apparent when accounting for individual level variation in athletic performance. Our results provide further evidence that meaningful trade-offs may be missed if individual variation in quality is disregarded.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it