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Record W2348475228 · doi:10.5539/mas.v10n6p21

The Impact of Sports on Athletic Performance

2016· article· en· W2348475228 on OpenAlexvenueno aff
Amir Torkashvand

Bibliographic record

VenueModern Applied Science · 2016
Typearticle
Languageen
FieldPsychology
TopicFlow Experience in Various Fields
Canadian institutionsnot available
Fundersnot available
KeywordsCronbach's alphaAthletesPopulationPsychologyData collectionApplied psychologyPhysical therapyMedicineClinical psychologyStatisticsMathematicsEnvironmental healthPsychometrics

Abstract

fetched live from OpenAlex

This study seeks to influence environment sports on the performance of athletes. Applied research is a descriptive survey. The population consisted of high-level of athletes in martial disciplines in Tehran (3 sports gym) were estimated at around 158 people. 113 athletes have been invited to respond to the questionnaire that they use randomly chosen sample of the population by Cochran formula. A questionnaire was used for data collection. The questionnaire consists of 3 parts. The first part included demographic information and the second part of the questionnaire contains 12 questions, including questions of appearance and the third part is consist of the standard questionnaire strategic performance of Thomas. Relaiability of questionnaire is achived with experts and stability of it was 0,966 by using of Cronbach's alpha coefficient in e SPSS software. The results show that the apparent and qualitative factors are directly affecting the performance of athletes.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.016
GPT teacher head0.311
Teacher spread0.295 · 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 source (direct Gemma or distilled Codex), 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
Published2016
Admission routes1
Has abstractyes

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