Bibliographic record
Abstract
ABSTRACT Introduction: Inspired by the traditional way of basketball training, this paper presents a new method of balance training, which can effectively improve basketball players’ coordination ability and shooting skills. Objective: Explore whether balance training can improve the coordination ability and shooting ability of athletes. Methods: Twenty players from a professional basketball team were randomly selected as volunteers, and divided into two groups randomly. The control group adopted the traditional basketball training model. In contrast, the experimental group adopted the new balance training method, introducing basketball training contents such as technical manipulation, psychological training, physical training, tactical analysis, and instant selection. Results: The hitting rate of shooting and jumping increased from 25.64±18.02 to 39.25±12.29, with a rate of change of 53.04%. The running time of the Illinois run was reduced from 15.79±1.08 seconds to 8.679±1.42 seconds, and the rate of change of time reached -45.04%. Conclusion: The balance training method plays an important role in promoting basketball players’ coordination and shooting ability, and can effectively improve the rate of these players. Level of evidence II; Therapeutic studies - investigation of treatment outcomes.
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) | 0.000 | 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) | 0.005 | 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".