Efficiency of cycling exercise: Quantification, mechanisms, and misunderstandings
Bibliographic record
Abstract
The energetics of cycling represents a well-studied area of exercise science, yet there are still many questions that remain. Efficiency, broadly defined as the ratio of energy output to energy input, is one key metric that, despite its importance from both a scientific as well as performance perspective, is commonly misunderstood. There are many factors that may affect cycling efficiency, both intrinsic (e.g., muscle fiber type composition) and extrinsic (e.g., cycling cadence, prior exercise, and training), creating a complex interplay of many components. Due to its relative simplicity, the measurement of oxygen uptake continues to be the most common means of measuring the energy cost of exercise (and thus efficiency); however, it is limited to only a small proportion of the range of outputs humans are capable of, further limiting our understanding of the energetics of high-intensity exercise and any mechanistic bases therein. This review presents evidence that delta efficiency does not represent muscular efficiency and challenges the notion that the slow component of oxygen uptake represents decreasing efficiency. It is noted that gross efficiency increases as intensity of exercise increases in spite of the fact that fast-twitch fibers are recruited to achieve this high power output. Understanding the energetics of high-intensity exercise will require critical evaluation of the available data.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".