The model of prediction of changes in the functional state of athletes engaged in hand-to-hand combat under the influence of the training load
Bibliographic record
Abstract
The purpose of the work is to develop a model for predicting changes in the functional state of athletes engaged in hand-to-hand combat, under the influence of a training load using psychophysiological indicators. Material and methods. The study involved 24 male athletes who are professionally engaged in hand-to-hand combat with full contact with the opponent (full contact), and 20 athletes. The average age of the athletes was 19-26 years. Research methods: analysis of scientific and methodological sources, psychophysiological, mathematical statistics, fuzzy logic. Results. The conducted studies proved the presence of significant differences in the values of psychophysiological indicators and the reaction to the training load of athletes with different levels of fitness, which made it possible to use these indicators to build a model for predicting the dynamics of a functional state. Changes in the functional state, determined by psychophysiological indicators, confirmed by corresponding changes in indicators of heart rate variability. The developed forecast model allows using two psychophysiological indicators (the time of a complex visual-motor reaction and the response index to a moving object), received to the load, to predict a change in the functional state of athletes engaged in hand-to-hand combat, with an overall accuracy of 95.5%. The forecast of changes in the functional state provides the trainer with the opportunity to timely adjust the volume of training loads and training regimen. Conclusions. Significant differences between groups of trained athletes and beginners in terms of the state of nervous processes (the time of a complex visual-motor reaction and the response index to a moving object) to the load were revealed, which allowed developing a model for predicting the functional reaction to the load in athletes with different levels of sportsmanship. Using the obtained model allows predicting changes in the functional state of athletes that will take place under the influence of the test load, according to psychophysiological indicators without using the load with an overall accuracy of 95.5%.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".