Bibliographic record
Abstract
Balance is the process of maintaining the body center of gravity vertically over the base of support and relies on rapid, continuous feedback and integration of afferent information coming from three sensory components, that is somatosensory, visual, and vestibular systems, resulting in smooth and coordinated neuromuscular actions. To investigate the effects of fatigue index on the static balance of sportsmen. A total of 51 male elite sportsmen from 4 different sports branches (Football players: 19, Volleyball players: 13, Skiers: 10 and Athletes: 9) participated in the study. The Wingate anaerobic power and capacity test was applied to induce fatigue. The Technobody isokinetic balance meter (Pro-Kin. CSMI) was used to measure pre- and post-fatigue static balance. There was a significant interaction between dependent (pre and post test balance) and independent (test statue and sport branches) variables were observed (F(63,1031.14) = 1.59, η2 = .07, p ˂ .05). Main effect results showed that pre-fatigue balance values were significantly different than post-fatigue balance values in all groups (F(1.14, 213.91) = 177.99, η2 = .49, p ˂ .05).Moreover, significant differences were identified between pre- and post-fatigue test results in sport branches (F(3.188) = 4.12, ƞ2 = .06, p < .05) and test statue (eyes open and closed) (F(3.188) = 3.32, ƞ2 = .05, p < .05). Bonferonni follow-up test indicated that there was a significant increase from footballers’ average static balance to the athletes’ average static balance (p ˂ .05). In test statue, pre fatigue eyes closed values were significantly different than post fatigue eyes opened values (p ˂ .05). Static balance training should be included in the training to be performed, fatigue static balance training should be performed in team and individual sports and the content of the training should be reorganized in line with the results obtained.
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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 | 0.001 |
| 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) | 0.002 | 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".