Critical Power Closely Approximates the Power Output at the Estimated Maximal Metabolic Steady State in Trained and Untrained Participants
Bibliographic record
Abstract
PURPOSE: This study compared estimations of critical power (CP) to maximal metabolic steady state (MMSS est ) to see if the differences in the predictions were affected by training status. METHODS: Twelve trained (6 females) and 12 untrained and not experienced with maximal testing (5 females) participants underwent i) a Step-Ramp-Step test to task failure to determine maximal oxygen consumption and peak power output, ii) 4-5 time to task failure trials at average power outputs (PO) ranging from 70 to 90% of peak power output for CP estimations, and iii) two to three 30-min constant PO rides to establish MMSS est as the highest PO at which oxygen consumption (V̇O 2 ) and blood lactate concentrations are stable. RESULTS: The PO associated with CP was significantly greater than that associated with MMSS est in both untrained (155 ± 39 W vs 147 ± 34 W, respectively) and trained (233 ± 37 W vs 225 ± 39 W, respectively) individuals ( P < 0.001). Both the untrained and trained groups displayed a similar and significant bias for MMSS est compared with CP (i.e., 7.5 W; P < 0.05), with 95% limits of agreement from -13 to 28 W, and -11 to 26 W for untrained and trained, respectively. CONCLUSIONS: These findings indicate that, despite a significant (albeit small) difference between CP and MMSS est , the CP model provided a close approximation of the PO associated with MMSS est in both untrained and trained participants, as the difference in PO was within the expected measurement error. Therefore, our results showed that, despite some small discrepancies between groups, the CP model fitting was not affected by training status and that previous testing experience with highly demanding exercise is not a key component of the quality of the prediction model.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".