Is maximal lactate steady state during intermittent cycling different for active compared with passive recovery?
Bibliographic record
Abstract
The purpose of this study was to analyze the effect of recovery type (passive vs. active) during prolonged intermittent exercises on the blood lactate concentration (MLSS) and work rate (MLSS(wint)) at maximal lactate steady state. Nineteen male trained cyclists were divided into 2 groups for the determination of MLSS(wint) using passive (maximal oxygen uptake = 58.1 ± 3.5 mL·kg(-1)·min(-1); N = 9) or active recovery (maximal oxygen uptake = 60.3 ± 9.0 mL·kg(-1)·min(-1); N = 10). They performed the following tests, on different days, on a cycle ergometer: (i) incremental test until exhaustion to determine maximal oxygen uptake; (ii) 2 to 3 continuous submaximal constant work rate tests (CWRT) for the determination of the work rate at continuous maximal lactate steady state (MLSS(wcont)); and (iii) 2 to 3 intermittent submaximal CWRT (7 × 4 min and 1 × 2 min, with 2-min recovery) with either passive or active recovery for the determination of MLSS(wint). MLSS(wint) was significantly higher when compared with MLSS(wcont) for both passive recovery (294.7 ± 32.2 vs. 258.7 ± 24.5 W, respectively) and active recovery groups (300.5 ± 23.9 vs. 273.2 ± 21.5 W, respectively). The percentage increments in MLSS(wint) were similar between conditions (passive = 13% vs. active = 10%). MLSS (mmol·L(-1)) was not significantly different between MLSS(wcont) and MLSS(wint) for either passive recovery (4.50 ± 2.10 vs. 5.61 ± 1.78, respectively) and active recovery (4.06 ± 1.49 vs. 4.91 ± 1.91, respectively) conditions. We can conclude that using a work/rest ratio of 2:1, MLSS(wint) was ∼10%-13% higher than MLSS(wcont), irrespective of the recovery type performed during prolonged intermittent exercises.
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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".