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Record W3014084780

Relationship of fat composition of throwing skill between the male cricket Player

2017· article· en· W3014084780 on OpenAlexaboutno aff
Sandeep Singh

Bibliographic record

VenueJournal of Emerging Technologies and Innovative Research · 2017
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsnot available
Fundersnot available
KeywordsCricketThrowingAnthropometryBody weightSkinfold thicknessComposition (language)Physical therapyMathematicsMedicineEngineeringAeronauticsBiologyArtInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

The purpose of this study was to determine relationship of body composition components with the throwing skill among cricket players. Total 35 male cricket players from the various colleges affiliated to Guru Nanak Dev University, Amritsar were selected to participate in the study. The subjects were assessed for height, weight and skinfold thicknesses. Height of the subjects was measured by using the standard anthropometric rod (HG-72, Nexgen ergonomics, Canada). Body weight of the subjects was measured with the help of portable weighing machine. Skinfold thicknesses of the body parts were measured with the Harpenden skinfold caliper. Throwing skill of the cricket players was assessed by AAHPERD cricket skill test battery. The statistical analysis revealed that the height, weight, BMI, skinfold thicknesses and various components of body composition did not show significant relationship with the throwing skill among the cricket 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 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)0.000
metaresearch head score (Gemma)0.001
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.201
GPT teacher head0.457
Teacher spread0.256 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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