Construction and analysis of dynamic model of discrete system of physical education teaching based on multi criteria side decision algorithm
Bibliographic record
Abstract
Based on the integrated application of intelligent wearable devices, this research obtains multi-dimensional real-time data in the movement process, obtains the human skeleton point data based on Openpose and carries out standardization preprocessing, extracts different posture features of the human body in combination with the geometric features of the skeleton space, and proposes an improved multi criteria decision tree intelligent algorithm theory of motion model and health evaluation system. On this basis, the statistical analysis and advantage comparison of multivariable motion data are carried out. Finally, the evaluation system of sports basic movement teaching based on multi criteria side optimization decision-making algorithm is established, and the functional design of each module is introduced. The research found that boys' upper limb strength and girls' cardiopulmonary endurance are the most important basic physical qualities that affect students' performance. The influence of lower extremity explosive force and cardiopulmonary function on boys' performance is only lower than that of upper extremity strength. The effect of girls' lower limb fast running ability on their performance is only inferior to that of their cardiopulmonary function; While increasing the strength of upper and lower limbs, boys can significantly improve the passing rate by properly improving cardiopulmonary endurance training. Proper improvement of girls' fast running ability and their cardiopulmonary endurance can significantly improve the passing rate of girls. And the motion intelligent recognition system in this study can overcome the self-occlusion of joint points when observing actions from a fixed perspective in a single perspective dataset.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".