MétaCan
Menu
Back to cohort
Record W2745433199 · doi:10.5430/jbar.v6n2p27

Influence of Clothing Materials on Protective Performance in Tennis

2017· article· en· W2745433199 on OpenAlexvenueno aff
Yuan Li, Hong Xie, Hongqiong Deng

Bibliographic record

VenueJournal of Business Administration Research · 2017
Typearticle
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsnot available
Fundersnot available
KeywordsClothingStructural engineeringElasticity (physics)EngineeringComputer scienceMaterials scienceComposite material

Abstract

fetched live from OpenAlex

The main objective of this study is to analyze influence of clothing materials on protective performance in tennis sportswear. Firstly, the paper presented a human protection model on tennis, and deduced two evaluation parameters to describe protection performance. Secondly, the experimental objects wore different gears made by different fabrics for tennis serve and the experiment data was collected to analyze the gears’ protection performance. Thickness, elasticity, fabric composite methods and wrapping types were set as independent variables of the gears. The ANOVA shows that these factors are of great significance to change the evaluation parameters of the upper limbs joints. Thickness of fabrics is more significant (P<0.01) on evaluation parameters than that of elasticity, especially for elbow (P<0.01), while elastic of fabric only affects peak momentum of elbow and wrist, but not obvious. The wrapping ways of pads are important factors to peak momentum changes. This research shows clothing material is an important element to design protective tennis gears.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.432
Threshold uncertainty score0.183

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
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 head0.085
GPT teacher head0.432
Teacher spread0.347 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Business Administration ResearchSame topicSports injuries and preventionFrench-language works237,207