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Record W2015011992 · doi:10.5539/res.v7n8p137

Assessment of the Students’ Physical Fitness Level and Metrological Justification of Motive Tests

2015· article· en· W2015011992 on OpenAlexvenueno aff
Nadezhda I. Palagina, Michael M. Polevshchikov, Yulia A. Dorogova, Maria L. Blinova, Andrey V. Zakamsky, Aleksandr M. Shraga, Eleonora A. Loskutova

Bibliographic record

VenueReview of European Studies · 2015
Typearticle
Languageen
FieldHealth Professions
TopicPhysical Education and Training Studies
Canadian institutionsnot available
Fundersnot available
KeywordsTest (biology)Physical fitnessPsychologyMetrologyTrunkPhysical testProcess (computing)Quality (philosophy)Computer scienceApplied psychologyMathematics educationMathematicsPhysical therapyStatisticsMedicine

Abstract

fetched live from OpenAlex

In this article there is considered the problem of an assessment of the students’ physical fitness level, which aren’t playing sports. Using of such assessment in the course of students’ physical training will allow to consider specific features of students, to define their weak and strengths and by that to provide their interest in improvement of their physical state’s level. The assessment of physical fitness level of the studying youth also promotes to the involvement of student’s youth in the self-improvement process and it helps to increase their physical fitness level. In order that such assessment was objective and real, it is necessary to carry out metrological justification of the test tasks entering to a complex. Metrological justification includes determination of coefficient of reliability and informational content of the offered test tasks in order that they could be used for an assessment of the separate parties of physical fitness. The factorial analysis of the offered tests is necessary to define possibilities of these tests to estimate this or that physical quality. In this article there are represented the results of the metrological assessment of test “battery”, that consists of the next 5 motive tasks: 10 minutes running; lifting of a trunk in 30 seconds lying on a back, hands along a trunk, feet aren’t fixed; an inclination forward, standing on a bench; bending of hands (push-up from a floor) in an emphasis on a lap in 30 seconds; transferring of a gymnastic stick straight arms for a back.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.168
Threshold uncertainty score0.288

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.497
GPT teacher head0.601
Teacher spread0.103 · 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
Published2015
Admission routes1
Has abstractyes

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