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Assessing and comparing the muscular endurance of the major spine-stabilising muscles of jumpers

2023· article· en· W4385174168 on OpenAlexaboutno aff
Madhu GR, K Sowmya, Praveen Kumar K

Bibliographic record

VenueInternational Journal of Physical Education Sports and Health · 2023
Typearticle
Languageen
FieldMedicine
TopicWinter Sports Injuries and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsTrunkTorsoSquatting positionPhysical therapyMedicineBack musclesPhysical medicine and rehabilitationSittingTest (biology)AnatomyBiology

Abstract

fetched live from OpenAlex

The aim of the present study was to assess and compare the muscular endurance of the major spine-stabilising muscles of jumpers. A total of 50 male jumpers were chosen as samples (n = 50) for data collection. The subjects are jumpers who represented and competed in intercollegiate and interuniversity championships. The selected subjects are between the ages of 18 and 25, and the study is restricted to the Dakshina Kannada district. For the purpose of data collection McGill’s torso muscular endurance test battery was used to measure the major spine stabilizing muscles. Using an advanced Excel workbook and a significance level of 0.05, the collected data was treated by mean, standard deviation, and t test. The study concluded that there is no difference in muscular endurance between the trunk flexion and trunk extensor muscles in male jumpers. The findings were represented using appropriate figures and tables.

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 score0.189
Threshold uncertainty score0.128

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 head0.035
GPT teacher head0.401
Teacher spread0.366 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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