Differences in efficiency between trained and recreational cyclists
Bibliographic record
Abstract
Controversy still exists in the literature as to whether cycling experience affects gross mechanical efficiency (GME). The aim of this study was to identify differences in efficiency between trained and untrained cyclists. Thirty-two participants, 16 trained (mean+/-SD: age, 33+/-4 y; height, 1.76+/-0.05 m; mass 75+/-10 kg; Wmax, 421+/-38 W; maximal oxygen uptake, 62.6+/-7.30 mL.kg(-1).min(-1)) and 16 untrained (22+/-3 y, 175+/-0.06 m, 76+/-10 kg, 292+/-34 W, 42.6+/-7.80 mL.kg(-1).min(-1)), performed two tests of cycling efficiency. One was at the relative workloads of 50% and 60% Wmax and the other was at a fixed workload of 150 W using an electrically braked cycle ergometer. Cadence was maintained at the cyclist's preferred rate throughout. All workloads lasted 10 min with data sampling in the final 3 min. GME was calculated from the gas data. GME was found to be significantly higher in the trained cyclists across all workloads (+1.4%; p=0.03). At workloads of 60% Wmax GME was significantly lower than work at 150 W (-0.8%; p=0.04), but not significantly different from 50% Wmax. These results show that differences do exist between trained and untrained cyclists, illustrating that training experience is a factor that warrants further investigation.
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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 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".