Assessment of the Students’ Physical Fitness Level and Metrological Justification of Motive Tests
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".