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Record W2922471910 · doi:10.5539/jel.v8n2p207

Relationship Between Jump Performance and Sport Ages in U16 Basketball Players

2019· article· en· W2922471910 on OpenAlexvenueno aff
Özlem Orhan, Sezen Çimen Polat, İmdat Yarım

Bibliographic record

VenueJournal of Education and Learning · 2019
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsnot available
Fundersnot available
KeywordsBasketballJumpBody mass indexBody weightMathematicsSignificant differenceAnimal sciencePsychologyPhysical therapyMedicineStatisticsPhysicsInternal medicineGeographyBiology

Abstract

fetched live from OpenAlex

This study was conducted to evaluate the jump performance of youth basketball players according to their sport ages. 26 male basketball players (14.1±1.6 year) who participated in the study were divided into two groups of sport ages of 4 and below (≤4) and 6 and above (≥6). The group with sports ages ≤4 consisted of 12 male basketball players with a height of 162±2.56 cm, a body weight of 51.4±3.04 kg, a body mass index of 19.4±0.74 kg/m². The other group with sports ages ≥6 consisted of 14 male basketball players with a height of 155.9±1.98 cm, a body weight of 45.7±1.85 kg, a body mass index of 18.8±0.69 kg/m. All basketball players’ squat jump (SJ) and countermovement jump (CMJ) were measured (Optojump Microgate Bolzano, Italy). The Mann Whitney U test was used to determine whether there were differences between groups in terms of T flighttimes and jump heights. Statistically significant level of p<0.05 was accepted. As a result of the study, no statistically significant difference was observed between the sport ages and SJ and CMJ splashes. In this respect, it can be considered that the Jump performance does not develop in parallel with the training age, and that the jump ability of this cause may be more related to motor skill and ability than the training age.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.214

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.024
GPT teacher head0.307
Teacher spread0.283 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2019
Admission routes1
Has abstractyes

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