Individual aerodynamic and physiological data are critical to optimise cycling time trial performance: one size does not fit all
Bibliographic record
Abstract
Abstract Cycling time trials are characterised by riders adopting positions to lessen the impact of aerodynamic drag. Aerodynamic positions likely impact the power a rider is able to produce due to changes in oxygen consumption, blood flow, muscle activation and economy. Therefore, the gain from optimising aerodynamics must outweigh the potential physiological cost. The aim was to establish the relationship between energy expenditure and aerodynamic drag, with a secondary aim to determine the reliability of a commercially available handlebar mounted aero device for measuring aerodynamic drag. Nine trained male cyclists volunteered for the study. They completed 4 × 3200 m on an outdoor velodrome where stack height was adjusted in 1 cm integers. The drag coefficient ( C d A ), oxygen consumption and aerodynamic-physiological economy (APE) were determined at each stack height, with data used to model 40 km TT performance. Small to moderate effect sizes (ES) in response to stack height change were found for C d A , APE and energy cost. The change in TT time was correlated to ∆aerodynamic drag and ∆APE. Meaningful impacts of change in stack height on C d A , APE, energy cost and predicted TT performance, are apparent with highly individualised responses to positional changes.
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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 | 0.020 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".