Quantifying metabolic energy contributions in sprint running: a novel bioenergetic model
Bibliographic record
Abstract
PURPOSE: To develop a bioenergetic model representing the dynamics of metabolic power-including aerobic, anaerobic lactic, and anaerobic alactic contributions-during 100-400 m sprints. This study calculates maximum anaerobic capacities using sprint data and assesses the model's ability to predict performance across various sprint distances. METHODS: Sprint energetics were estimated applying di Prampero et al. (J Exp Biol 208:2809-2816, 2005) method using velocity and time-split data from the 2009 World Athletics Championships to model metabolic power over the men's and women's 100-200-400 m events. Aerobic power was modeled with an exponential function, anaerobic lactic power with a bi-exponential function, and anaerobic alactic power with a log-normal function. Maximal anaerobic lactic and alactic capacities were estimated from available performances. Simulations were made to predict the distance traveled by hypothetical male and female athletes achieving World Championship performances on the 100-200-400 m. RESULTS: (female). Simulations of distance traveled revealed mean absolute errors of 0.31% and 1.63% for male and female, respectively. Higher female errors likely stem from underestimating anaerobic lactic contribution due to male-derived parameters and limited available data. CONCLUSION: This model aligns closely with theoretical bioenergetic principles and experimental findings, providing valuable insights that improve our understanding of sprint running energetics and performance. Further refinements, incorporating female-specific parameters and collecting data from various distances, could broaden the model's applicability.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".