Energy Systems and Performance Endurance in Cyclists According to the Type of Course
Bibliographic record
Abstract
Research has shown a significant positive contribution of the anaerobic system on endurance performance in 5 and 10 km running race. However, this has not yet been verified in cycling with longer periods of exercise on different types of course. PURPOSE: To determine the contribution of the anaerobic system and other energy systems on a simulated 28 km road cycling flat course compared to a 20 km uphill course. METHODS: Ten (10) cyclists completed a peak aerobic power test, a 3-min all-out test to measure critical power (CP), peak power (PP), anaerobic capacity (W’), and finally 2 simulated isoenergetic time-trials on a flat course of 28 km and the other an uphill 20 km (442.1 m vertical gain). The route for both courses were similarly composed of 5 isoenergetic laps (4 km vs. 5.6 km) performed on a Computrainer. RESULTS: W’ and PP were inversely correlated with the relative average power (PMr-20 km) (W’ : r = -0.64, p<0.01; PP : r = -0.73, p0.05 for flat vs. ascending), but anaerobic work (Wana) during the uphill 20 km was higher than in the flat 28 km (18.53±10.02 vs. 7.91±4.55 kJ, p<0.001). However, the aerobic work (Wae) in the 20 km was lower than during the 28 km (632.37±57.42 vs. 690.22±57.40 kJ, p<0.05). Kinetics of PMr-20 km and of PM-28 km showed a parabolic performance strategy on both courses (F (1, 9) = 22.08, p<0.01; F (1, 9) = 42.03, p<0.01). CONCLUSION: Anaerobic metabolism contributed to a higher proportion in the cycling ascending time trial. The course characteristics may influence the recruitment energy systems with a same energy expenditure.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".