Morning–evening differences in response to exhaustive severe-intensity exercise
Bibliographic record
Abstract
The aim was to investigate the effect of time of day on 4 variables that are related to sport performance. Twenty healthy young men (mean ± SD: 22 ± 3 years, 1.78 ± 0.08 m, 72.0 ± 7.0 kg) performed exhaustive severe-intensity cycle ergometer tests at 278 ± 35 W (3.8 ± 0.4 W·kg(-1)) in the morning (between 0630 h and 0930 h) and in the evening (between 1700 h and 2000 h). Despite that gross efficiency was lower in the evening (estimated oxygen demand was 6% higher, P < 0.05), time to exhaustion was 20% greater (P < 0.01) in the evening (329 ± 35 s) than in the morning (275 ± 29 s). Performance in the evening was associated with a 4% higher (P < 0.01) maximal oxygen uptake (54 ± 7 mL·kg(-1)·min(-1) vs. 52 ± 6 mL·kg(-1)·min(-1), for the evening and the morning, respectively) and a 7% higher (P < 0.01) anaerobic capacity (as reflected by maximal accumulated oxygen deficit: 75 ± 9 mL·kg(-1) vs. 70 ± 7 mL·kg(-1), for the evening and the morning, respectively). In addition, oxygen uptake kinetics was faster in the evening, which resulted in slower utilization of the anaerobic reserves. It is concluded that modest morning-evening differences in maximal oxygen uptake, anaerobic capacity, and oxygen uptake kinetics conflate to produce a markedly longer performance in the evening than in the morning. Time of day must be considered for exercise testing and perhaps for exercise training.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".