Construction and analysis of dynamic model of discrete system of physical education teaching based on multi criteria side decision algorithm
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 it