Nonlinear kinetic modeling of training load and recovery cycle in martial arts sparring athletes
Bibliographic record
Abstract
The monitoring of training load and recovery cycle of Wushu Sanshou athletes is a long-term and fundamental work for sports teams.The article introduces the parameters of resting heart rate, ventricular muscle contractility, arterial wall and maximal oxygen uptake VO2max as monitoring indexes, and designs a real-time monitoring method of physical training load data based on graph convolution network.Subsequently, through the flow level variables (BFL, TLQ, BRQ), flow rate variables (BFLI, BFLD, TLQI, BRQI), auxiliary variables (TT, TI, RT, RM), exogenous variables (RYN), and the causal relationship between the elements of each variable of the Wushu sparring training function monitoring system, we constructed a nonlinear system of the training load and recovery cycle of the Wushu sparring athlete Dynamics model.Using the real-time monitoring model of this paper to monitor the wushu sparring athletes, in the third minute of the experiment, the real-time monitoring system predicted that the heart rate was 90, and the adjusted heart rate using the model of this paper was 90, which was consistent with the actual monitored heart rate.It can be concluded that the model of this paper can well monitor the training load of martial arts sparring athletes.Through experimental simulation, the article concludes that both the strong physical fitness program and the strong training program can be beneficial to the training of wushu sparring athletes.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".