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Record W4406075354 · doi:10.58914/ijyesspe.2024-9.2.10

Effects of Soccer Training Programme on Physical Fitness Variables of School Level Tribal Students

2025· article· en· W4406075354 on OpenAlexaff
J Rakshit, Sukanta Saha

Bibliographic record

VenueIndian Journal of YOGA Exercise & Sport Science and Physical Education · 2025
Typearticle
Languageen
FieldHealth Professions
TopicSports and Physical Education Research
Canadian institutionsCanarie
Fundersnot available
KeywordsTraining (meteorology)Physical fitnessPsychologyMathematics educationPhysical therapyMedical educationGeographyMedicineMeteorology

Abstract

fetched live from OpenAlex

The aim of the study was to find out the effects of specific soccer training programme on physical fitness variables namely agility, speed and endurance of school level tribal students. For the purpose of the study 60 tribal students were selected from Pandit Raghunath Murmu Abasik School, Nangla, Susunia, Bankura, West Bengal, India.The age of the subjects were 14 to 16 years as per school record. Sixty tribal students ware sub divided into two group’s i.e. experimental tribal (N=30) and control tribal (N=30). The training was given to the experimental group 4 day per week, par day one session for the period of the eight weeks. The content group was not provided any kind of specific soccer training. Pair T-Test was used to calculate the acquired data on agility, speed and endurance. Level of significance was set at 0.05 level. Result revealed that experimental group significantly improved their agility, speed and endurance performance after 8 weeks specific soccer training but the improvement of control group was not significant.

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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.000

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 head0.065
GPT teacher head0.477
Teacher spread0.412 · 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
Published2025
Admission routes1
Has abstractyes

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