Bibliographic record
Abstract
BACKGROUND: For years, the effects of music on exercise performance have been researched. Recovery is extremely important for athletes, and therefore any factor that could affect it is of importance. OBJECTIVE: To assess the influence of listening to music on recovery after an anaerobic-exercise. METHOD: 25 male athletes (age 21.76 [Formula: see text] 1.84 years) visited the laboratory on two occasions over a week. They performed the Wingate Anaerobic Test (WAnT) on two identical conditions but recovery was conducted ‘with’ and ‘without’ listening to music. Blood lactate concentration values were determined at 1, 5, 10 and 15 minutes during the recovery from the exercise. Heart rate (HR) values were determined every minute of the 15 minutes of recovery. RESULTS: There was no difference in the mean blood lactate concentration and HR during the recovery with and without music ([Formula: see text] 0.05). Results showed no significant differences between 2 recovery conditions in heart rate or blood lactate. CONCLUSIONS: Music cannot improve recovery after anaerobic performance and it cannot be used as a mean to enhance recovery after an anaerobic-performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".