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Record W4396640115 · doi:10.53555/sfs.v10i3.2678

Effect of Resistance Circuit Training and Intensive Interval Training on Speed of Veer Narmad South Gujarat University Kho-Kho Players

2023· article· en· W4396640115 on OpenAlexvenueno aff
Patel Prafulkumar Rameshbhai, Hemant Pandya

Bibliographic record

VenueJournal of Survey in Fisheries Sciences · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicSports Analytics and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsTraining (meteorology)Circuit trainingResistance trainingInterval (graph theory)Resistance (ecology)PsychologyMathematicsPhysical therapyMedicineGeographyBiologyMeteorology

Abstract

fetched live from OpenAlex

The purpose of this research study was Effect of Resistance Circuit Training and Intensive Interval Training on Speed of Veer Narmad South Gujarat University Kho-Kho Players Male players of Kho-Kho selected at school level in Veer Narmad South Gujarat University District were selected in the present study. Total 90 male players were selected as subjects for the sample of the present study, in which 30 players were included in the Resistance Circuit training group, 30 in Intensive Interval training group and 30 players were included in the control group. The male players of 13 to 17 years age group were included in the present study. In this research study Speed was measured by 50 Yard Desh Run. Statistical technique such as analysis of covariance was applied to know the effects on Resistance Circuit training group and Intensive Interval training group. Mean difference was examined at 0.05 levels by using Least Significant Difference (Post Hoc) Test. The conclusion of which was seen as follows. Remarkable improvement was found in Speed of selected subjects by 12 weeks systematic Resistance Circuit training and Intensive Interval training programmes.

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)0.006
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.397

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.260
GPT teacher head0.261
Teacher spread0.001 · 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
Published2023
Admission routes1
Has abstractyes

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