Time to exhaustion during cycling is not well predicted by critical power calculations
Bibliographic record
Abstract
Three to 5 cycling tests to exhaustion allow prediction of time to exhaustion (TTE) at power output based on calculation of critical power (CP). We aimed to determine the accuracy of CP predictions of TTE at power outputs habitually endured by cyclists. Fourteen endurance-trained male cyclists underwent 4 randomized cycle-ergometer TTE tests at power outputs eliciting (i) mean Wingate anaerobic test (WAnTmean), (ii) maximal oxygen consumption, (iii) respiratory compensation threshold (VT2), and (iv) maximal lactate steady state (MLSS). Tests were conducted in duplicate with coefficient of variation of 5%–9%. Power outputs were 710 ± 63 W for WAnTmean, 366 ± 26 W for maximal oxygen consumption, 302 ± 31 W for VT2 and 247 ± 20 W for MLSS. Corresponding TTE were 00:29 ± 00:06, 03:23 ± 00:45, 11:29 ± 05:07, and 76:05 ± 13:53 min:s, respectively. Power output associated with CP was only 2% lower than MLSS (242 ± 19 vs. 247 ± 20 W; P < 0.001). The CP predictions overestimated TTE at WAnTmean (00:24 ± 00:10 mm:ss) and MLSS (04:41 ± 11:47 min:s), underestimated TTE at VT2 (–04:18 ± 03:20 mm:ss; P < 0.05), and correctly predicted TTE at maximal oxygen consumption. In summary, CP accurately predicts MLSS power output and TTE at maximal oxygen consumption. However, it should not be used to estimate time to exhaustion in trained cyclists at higher or lower power outputs (e.g., sprints and 40-km time trials). Novelty CP calculation enables to predict TTE at any cycling power output. We tested those predictions against measured TTE in a wide range of cycling power outputs. CP appropriately predicted TTE at maximal oxygen consumption intensity but err at higher and lower cycling power outputs.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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".