The effect of training volume and intensity on competitive cyclists’ efficiency
Bibliographic record
Abstract
The impact of different intensity training on cycling efficiency in competitive cyclists is unknown. Twenty-nine endurance-trained competitive male cyclists completed 3 laboratory visits during a 12-week training period. At each visit, their cycling efficiency and maximal oxygen uptake were determined. After the first visit, cyclists were randomly split into 2 groups (A and B). Over the first 6 weeks, between tests 1 and 2, group A was prescribed specific high-intensity training sessions, whereas group B was restricted in the amount of intensive work undertaken. After test 2 and for the second 6-week period, group B was allowed to conduct high-intensity training. Gross efficiency (GE) increased in group A (+1.6 +/- 1.4%; p < 0.05) following the high-intensity training, whereas no significant change was seen in group B (+0.1 +/- 0.7%; p > 0.05). Group B cyclists increased their GE between tests 2 and 3 (+1.4 +/- 0.8%; p < 0.05) but no changes in GE were observed in group A over this period (+0.4 +/- 0.4%; p > 0.05). Delta efficiency (DE) did not change significantly in either group across the study period. This study demonstrates that GE is increased following high-intensity training in competitive male cyclists after 12 weeks.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".