Do Critical and Functional Threshold Powers Equate in Highly- Trained Athletes?
Bibliographic record
Abstract
International Journal of Exercise Science 14(4): 45-59, 2021. The purpose of this investigation was to determine whether Critical Power (CP) and Functional Threshold Power (FTP) can be used interchangeably for a highly-trained group of cyclists and triathletes. CP was ascertained using multiple fixed load trials and FTP determined from a single cycling trial. Three different models for the determination of CP were initially addressed, one hyperbolic (Hmodel) and two linear (Jmodel and Imodel). The Jmodel was identified as most appropriate for a comparison with FTP. The Jmodel and FTP were not found to be interchangeable as ANOVA detected significant differences (282 ± 53 vs. 266 ± 55 W, p < 0.001) between these indices and the associated Bland-Altman 95% limits of agreement exceeded those set a priori. As the Jmodel was found to be consistently higher than FTP, a correction factor was posited to anticipate CP from FTP in this homogenous group of athletes using the mean bias (16 W). An alternate method for assessing CP trial intensities using Dmax as a proxy for ventilatory threshold is also proposed. The concept of both CP and FTP representing a maximal metabolic steady-state requires further investigation as the mechanical power at CP was significantly greater than at FTP.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".