Estimate Anaerobic Work Capacity and Critical Power with Constant-Power All-Out Test
Bibliographic record
Abstract
Background: The critical power model (CPM) is used extensively in sports to characterize fitness by estimating anaerobic work capacity (W’) and critical power (CP). Traditionally, estimates of CP and W’ require repeated, time-consuming tests. Alternatively, a 3 min all-out test yields good estimates of W’ and CP. However, adoption of the 3 min protocol for regular fitness monitoring is deterred by the mentally/physically strenuous nature of the test. Objective: We propose to examine an alternative single-session testing protocol that can accurately estimate critical power model parameters. Methods: Twenty-eight healthy competitive athletes (cyclists or triathletes) (mean ± SD: age: 38.5 ± 10.4 years, height: 177.9 ± 8.6 cm, mass: 73.4 ± 9.9 kg) participated in 5 sessions on a Lode cycle ergometer in isokinetic mode within a 2-week period. A 3 min all-out test (3MT) was conducted on the first visit to determine CPM parameters from which power outputs for 4 subsequent constant-power plus all-out tests (CPT) were selected to result in exhaustion in 1–10 min. The subjects were to maintain the prescribed power output as consistently as possible at their preferred race cadence. Once the power output could no longer be maintained for more than 10 s, the subjects were instructed to produce an all-out effort. Tests were terminated after power output fell to an asymptote which was sustained for 2 min. Results: The CPM parameters for all of the CPT durations were compared to the traditional CP protocol (significant parameter differences were identified for all CPT durations) and the 3MT (only CPT durations > 3 min were different [3–6 min test, p < 0.01; >6 min test, p < 0.01]). CPT does not estimate traditional CP and W’ parameters well. However, the CPT with a duration < 3 min accurately estimates both parameters of a 3MT. Conclusion: Therefore, CPT has the capacity to serve as an alternative tool to assess CP parameters.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".