Lactate Threshold does not Influence Metabolic Responses during Exercise in Cyclists
Bibliographic record
Abstract
The purpose of this study was to compare plasma markers of metabolic stress and other physiological parameters during prolonged endurance exercise of different intensity in trained subjects possessing a "high" or "low" lactate threshold (LT) expressed as a percentage (%) of peak power output (PPO). Fifteen trained male cyclists completed an incremental exercise test for determination of PPO and the LT (% PPO). Each subject then completed a 90-min and 20-min exercise trial at an intensity representing 75 and 85 % of PPO, respectively. Blood lactate (La), as well as plasma hypoxanthine (Hx) and uric acid (UA) were measured during each exercise trial. The responses in two groups, one (n = 8) with a LT approximately 65 % PPO (LT (low)), the other group (n = 7) with a LT approximately 75 % (LT (high)) (p < 0.01), were then compared. With the exception of UA, La and Hx increased significantly (p < 0.01) throughout each exercise trial compared to rest. However, there were no significant differences in each trial between the two groups of cyclists. There were also no significant differences in the other physiological parameters in each exercise trial between the subjects in LT (low) and LT (high). This study demonstrates that in trained cyclists homogeneous in terms of PPO, plasma markers of metabolic demand during prolonged exercise are not influenced by the LT when measured in an incremental exercise test.
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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".