Relationship of speed and unilateral vertical jump performance of basketball athletes
Bibliographic record
Abstract
Abstract Basketball is a sport practiced around the world in this way, performances tests especially with low-cost and accessible are suggested to adjust the training. The 00aim of present study was to evaluate and correlate the speed and jump performance of lower limbs of basketball athletes from the Praia Grande, city of São Paulo, Brazil. Twelve male athletes (6.92 ± 2.57 years of pratice) from the Basketball team of Praia Grande City, aged 18.7 ± 0.6 years, height 1.85 ± 0.06, body mass 83.66 ± 10.16 kg were evaluated. The athletes were submitted to test sessions using the protocol for evaluating the maximum speed of 20 meters and the unilateral vertical jump. The speed on 20 meter test was 3.53 ± 0.20 seconds. Although an asymmetry of 17.81 ± 14.64% was found, no statistical difference (p = 0.817) was found between the dominant (36.50 ± 7.36 cm) and non-dominant (35.92 ± 5.63 cm) of vertical jump performance. Additionally, correlation between 20-meter test and values jump was found to dominant leg (p = 0.042) but not to non-dominant leg (p = 0.704). In conclusion, although asymmetry was found between members of the dominant and non-dominant side of the pitch, did not show any difference in the vertical jump.
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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".