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Record W2898838381 · doi:10.5430/wje.v8n5p211

Characteristics of Muscle Power and Agility in Top-Level Junior Soft Tennis Players

2018· article· en· W2898838381 on OpenAlexvenueno aff
Hiroki Aoki, Shinichi Demura, Masakatsu Nakada, Tamotsu Kitabayashi

Bibliographic record

VenueWorld Journal of Education · 2018
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsnot available
Fundersnot available
KeywordsTrunkPhysical therapyPsychologyMedicine

Abstract

fetched live from OpenAlex

This study examined the characteristics, such as muscle power and agility, of top-level junior soft tennis players. 36Japanese top-level junior (under-14) boys soft tennis players (age 13.4 ± 0.8 years,height 165.0 ± 9.7cm,andweight 53.9 ± 11.1 kg) with experience in international meet participation and 25 junior boys soft tennis players(age 13.1 ± 0.7 years,height 158.2 ± 9.5cm,weight 47.0 ± 8.2 kg) with experience in prefectural meetparticipation were the subjects of this study. Medicine ball (2 kg) throws to the right or left by trunk rotation andforward and backward by trunk extension (exercises recommended by Japan’s soft tennis association) were selectedto evaluate the muscle power of the subjects. Side step, spider, and front–back shuttle run tests (used by an Americantennis association) were selected to test the agility of the subjects. The top-level athletes were significantly superiorto the other junior players in the forward medicine ball throw, side step, and spider tests. These results suggest thatmuscle power in the forward direction and agility when moving to the right, left or diagonal direction are moredeveloped in top-level soft tennis players than in other players.

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)0.000
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 score0.065
Threshold uncertainty score0.167

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.309
Teacher spread0.284 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

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