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Record W4223624742 · doi:10.3390/app12083811

The Influence of the Practiced Karate Style on the Dexterity and Strength of the Hand

2022· article· en· W4223624742 on OpenAlexaff
Jacek Wąsik, Dariusz Bajkowski, Gongbing Shan, Robert Podstawski, Wojciech J. Cynarski

Bibliographic record

VenueApplied Sciences · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicMartial Arts: Techniques, Psychology, and Education
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsMartial artsGrip strengthPsychologyTest (biology)Style (visual arts)Physical therapyMedicineArtVisual arts

Abstract

fetched live from OpenAlex

Background: The need for a strong grip in ‘ground’ martial arts is undisputed, but it is not obvious in karate. It may be expected that in the case of advanced karate fighters where dynamic combat movements dominate, the level of speed skills will be high. However, does the karate style affect the Ditrich rod dexterity and the strength of the players’ handshake? Methods: 39 participants were analyzed, all of whom were elite karate fighters—21 in the Kyokushin style (age: 31.4 ± 6.3; body weight: 77.2 ± 18.2 kg) and 18 in the Shotokan style (age: 23.3 ± 11.8 years; body weight: 70.9 ± 14.2 kg). They performed the following: a test of reaction speed and dexterity with a Ditrich rod, and a hand grip strength test with a dynamometer. Results: The data shows that there is no difference in the Ditrich rod test for both the left and right hand among the analyzed Kyokushin and Shotokan fighters. Significant differences were recorded in the grip strength of both hands (p < 0.05). There is a positive correlation between the strength of the grip on both hands (r = 0.593; p < 0.05). Discussion: Kyokushin-style karate players have a higher grip strength than those trained in the Shotokan style. Perhaps this is due to differences in the preparation for fights. The analysis showed no statistical significance in the Ditrich rod test. It is probable that the level of dexterity in karate is independent of the training canon.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.705
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0040.003
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.028
GPT teacher head0.328
Teacher spread0.300 · 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; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
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

Citations7
Published2022
Admission routes1
Has abstractyes

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