Bibliographic record
Abstract
Research findings have indicated different influence of caffeine consumption on athletic performance.The purpose of this research was evaluating influence of caffeine consumption on some cardiovascular factors including heart rate and blood pressure during rest, end of exercise and recovery.Subjects of this study were 24 female vollybal athletes with at least one year experience of athletic participation .whoparticipated voluntarily in this study.After recording of some subjects characteristics and assurance of not being sensitive to caffeine, two exercise tests were performed in separate weeks.In first test placebo and in second test caffeine (5 mg/kg) was consumed (one hour before trail) by subjects.heart rate and blood pressure were measured during rest, at the end of exercise testing and during 3rd and 5th minutes of recovery of submaximal test.Maximal oxygen consumption (VO2max) was measured using submaximal cardiorespiratory test on Monark ergometer.T paired test was used for analysis of data.Results: caffeine consumption was effective on Vo2max, hear rate during rest and end of exercise, and end exercise blood pressure (P>0.05).caffeine consumption caused increasing heart rate during 3rd and 5th minutes of recovery, blood pressure during rest, 3rd and 5th minutes of recovery in athletes(p≤o.05).
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.948 | 0.930 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".