The Comparison of Lower and Higher Extremity Anaerobic Power Capacities of Indoor Athletes
Bibliographic record
Abstract
The objective of this study is the comparison of lower and higher extremity anaerobic power capacities of indoor athletes. 25 handball players (13 females, 12 males), 22 basketball players (10 females, 12 males) and experimental control group (10 females, 13 males, total 23), it means 70 athletes from amateur leagues participated in the research. Lower extremity anaerobic powers of participants were measured by wattbike pro power bike. Higher extremity anaerobic powers of participants were measured by myotest. Data statistics were done by Windows SPSS 22.0. For dispersion of averages and standart deviations, descriptive statistics methods were used. In order to see the effects of gender and branch variances of participants on lower and higher extremity, Two-Way Manova analysis was used. Moreover, in order to see differences clearly, Screen Plot graphics were performed. In higher extremity measurements analysis, for 3 higher extremity values except velocity, male participants had higher averages than female participants do (p < 0.05). Based on Pairwise Comparisons results for branches, both Power Wkg and Power Max W kg values, handball participants had significant higher averages than control group participants did (p < 0.05). For lower extremity variances, female participants had lower averages than male participants. For Powermass values, handball players had higher averages than control group did. For Powerpeak and Power Average variances, control group participants had significant lower averages than both basketball players and handball players did (p < 0.05). For all values, there were no significant differences between handball and basketball players, except male height measurements (p > 0.05).
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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 | 0.001 | 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) | 0.004 | 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".