Reference values and improvement of aerodynamic drag in professional cyclists
Bibliographic record
Abstract
The aims of this study were to measure the aerodynamic drag in professional cyclists, to obtain aerodynamic drag reference values in static and effort positions, to improve the cyclists' aerodynamic drag by modifying their position and cycle equipment, and to evaluate the advantages and disadvantages of these modifications. The study was performed in a wind tunnel with five professional cyclists. Four positions were assessed with a time-trial bike and one position with a standard racing bike. In all positions, aerodynamic drag and kinematic variables were recorded. The drag area for the time-trial bike was 31% higher in the effort than static position, and lower than for the standard racing bike. Changes in the cyclists' position decreased the aerodynamic drag by 14%. The aero-helmet was not favourable for all cyclists. The reliability of aerodynamic drag measures in the wind tunnel was high (r > 0.96, coefficient of variation < 2%). In conclusion, we measured and improved the aerodynamic drag in professional cyclists. Our results were better than those of other researchers who did not assess aerodynamic drag during effort at race pace and who employed different wheels. The efficiency of the aero-helmet, and the validity, reliability, and sensitivity of the wind tunnel and aerodynamic field testing were addressed.
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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 | 0.006 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".