A comparison of critical power and the respiratory compensation point at slower and faster pedaling cadences
Bibliographic record
Abstract
We investigated whether pedal cadence (60 vs. 100 rpm) affects oxygen uptake (V̇O2) and power output (PO) at two indexes of the heavy-to-severe-intensity domain boundary (i.e., critical power (CP) and respiratory compensation point (RCP)) and their correspondence. Fourteen adults (7 females, 23 ± 2 years) cycled at 60 and 100 rpm during: (i) a “step-ramp-step” protocol to identify V̇O2 and PO at RCP; (ii) 4–5 exhaustive constant-PO bouts for CP identification; and (iii) a constant-power bout at CP to identify V̇O2 at CP. Separate two-way repeated measures Analysis of variance assessed whether V̇O2 and PO were affected by index (CP vs. RCP) and cadence (60 vs. 100 rpm). The V̇O2 was not affected by index (mean difference (MD) = 73 ± 197 mL·min−1; p = 0.136) but there was an index × cadence interaction ( p = 0.014), such that V̇O2 was higher at 100 versus 60 rpm for CP (MD = 142 ± 169 mL·min−1; p = 0.008), but not RCP ( p = 0526). The PO was affected by cadence (MD = 13 ± 9 W; p < 0.001) and index (MD = 8 ± 11 W; p = 0.016), with no cadence × index interaction ( p = 0.168). The systematic bias in PO confirms cadence-specificity of CP and RCP. The relationship between these indexes and their change in unison in PO suggests a mechanistic link between these two heavy-to-severe domain boundary candidates.
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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".