Lower limb Kinematic analysis to Le Petit Echappe´ by using two different Pointe Training Pointe and Professional Pointe in ballet
Bibliographic record
Abstract
This research aims to Know The Lower limb Kinematic analysis to Le Petit Echappe´ by using two different Pointe Training Pointe and Professional Pointe in ballet, The researcher used the descriptive approach to suit the nature of the study, and The basic study sample was chosen by the intentional method from the students of the fourth year at the Faculty of Physical Education for Boys - Girls in Port Said, and the sample included (5) students. the Conclusions was Determine the kinematic parameters of Le Petit Echappe´'s performance in ballet using the Training Pointe. Determine the kinematic parameters of Le Petit Echappe´'s ballet performance using Performance Pointe. Performance with Performance Pointe, loosening the angle of the feet during the three stages of performance. Performance with Training Pointe is based on working muscles significantly without input from th e used shoe. Performance using Training Pointe is anti-performance and not helpful, unlike performance with Performance Pointe. A critical biodynamic variable in Le Petit Echappe´'s performance in ballet jump (the vertical wheel and the collecting acceleration).
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.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".