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Record W2886637125 · doi:10.5430/wje.v8n4p54

Leg Volume and Mass Scales of Elite Male and Female Athletes in Some Olympic Sports

2018· article· en· W2886637125 on OpenAlexvenueno aff
Sevde Mavi Var, İrfan MARANGOZ

Bibliographic record

VenueWorld Journal of Education · 2018
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsnot available
Fundersnot available
KeywordsBasketballAthletesFootballPhysical therapyFootball playersElite athletesPsychologyMedicineGeography

Abstract

fetched live from OpenAlex

This study aims to scale average leg volume and mass scales of elite male and female athletes in some olympicsports. A total of 280 elite athletes comprising of 200 males and 80 females studying at School of Physical Educationand Sports at University of Kirsehir Ahi Evran voluntarily participated in this study. Frustum and Hanavan methodswere used to determine leg volume and mass, respectively. SPSS 22.0 package program for Windows was used fordescriptive statistics analysis of the study. The present study found average leg volume and mass scale of maleathletes in football, basketball, volleyball, handball, gymnastics and wrestling and female athletes in football,basketball, volleyball, handball, gymnastics, box, taekwondo and tennis. It was observed in the related scale that legvolume of the athletes in the lowest weight classes in weight sports were lower. In other words, leg volume and massof the athletes were in direct proportion to their weight class. When the scale of female athletes is analyzed, it can benoted that volleyball players have the highest leg volume and mass among team sports 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 score0.046
Threshold uncertainty score0.195

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 head0.013
GPT teacher head0.284
Teacher spread0.271 · 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

Citations3
Published2018
Admission routes1
Has abstractyes

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