Effect of aerobic training status on both maximal lactate steady state and critical power
Bibliographic record
Abstract
This study aimed at assessing the sensitivity of both maximal lactate steady state (MLSS) and critical power (CP) in populations of different aerobic training status to ascertain whether CP is as sensitive as MLSS to a change in aerobic fitness. Seven untrained subjects (UT) (maximal oxygen uptake = 37.4 ± 6.5 mL·kg(-1)·min(-1)) and 7 endurance cyclists (T) (maximal oxygen uptake = 62.4 ± 5.2 mL·kg(-1)·min(-1)) performed an incremental test for maximal oxygen uptake estimation and several constant work rate tests for MLSS and CP determination. MLSS, whether expressed in mL·kg(-1)·min(-1) (T: 51.8 ± 5.7 vs. UT: 29.0 ± 6.1) or % maximal oxygen uptake (T: 83.1 ± 6.8 vs. UT: 77.1 ± 4.5), was significantly higher in the T group. CP expressed in mL·kg(-1)·min(-1) (T: 56.8 ± 5.1 vs. UT: 33.1 ± 6.3) was significantly higher in the T group as well but no difference was found when expressed in % maximal oxygen uptake (T: 91.1 ± 4.8 vs. UT: 88.3 ± 3.6). Whether expressed in absolute or relative values, MLSS is sensitive to aerobic training status and a good measure of aerobic endurance. Conversely, the improvement in CP with years of training is proportional to those of maximal oxygen uptake. Thus, CP might be less sensitive than MLSS for depicting an enhancement in aerobic fitness.
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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) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 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".