Numerical simulation study of physical fitness changes in track and field athletes with different training cycles
Bibliographic record
Abstract
This paper realizes the detection of changes in physical fitness of track and field athletes in different training cycles by monitoring their sports training functions.The method used is the time series model ARMA.The athletic training function time series data were preprocessed to fit the ARMA (p,q) model, and the optimal time series fitting model was selected by examining the coefficient of determination, AIC criterion, and SC criterion.Four biochemical indexes, hemoglobin, urea, creatine kinase, and testosterone, were selected as the content of training monitoring for track and field athletes, and the ARMA(1,1) model was selected to analyze the changes in physical fitness of track and field athletes in different training cycles.Taking the hemoglobin index (HB) as an example, through the numerical simulation of the time series of HB levels of 16 track and field athletes preparing for the 15th National Games in Guangdong Province, it can be learned that the change trends of male and female track and field athletes are basically the same throughout the whole year training cycle.From the first cycle, the athletes' Hb levels began to decrease, fell to the lowest level in the third cycle, and rebounded in the fourth cycle, reaching the highest Hb level in the winter training period.
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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 | 0.000 | 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".