Bibliographic record
Abstract
The aim of this study was to investigate the effects of agility training on reaction time in fencers. Accordingly, 48 athletes (24 females and 24 males) actively involved in fencing participated in the study. Fencers were divided into two groups as 24 fencers in the Agility Training Group (ATG: 12F, 12M) (aged 11.95 years, sports age 2.54 years, height 159 cm, body weight 48.08 kg and body mass index 18.81 kg/m²) and Conventional Training Group (CTG: 12F, 12M) (aged 12.12 years, sports age 2.20 years, height 156.54 cm, body weight 46.25 kg and body mass index 18.81 kg/m²). There was a statistically significant difference between the pre-test and post-test values in agility, vertical jump, simple reaction time and multiple reaction time tests in the ATG (p<0.05). There was a statistically significant difference between the pre-test and post-test values in agility and vertical jump tests in the CTG (p<0.05). There was a statistically significant difference in the pre-tests between the groups in the simple reaction time test (p<0.05). There was a statistically significant difference in post-tests between the groups in the agility, simple reaction time and multiple reaction time test values (p<0.05). Based on the results of the study, it was found that simple and multiple reaction time could be positively affected by active-reactive agility training applications.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.004 | 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".