The effect of progressively increased physical efforts on visual evoked potentials in volleyball players and non-athletes
Bibliographic record
Abstract
We assessed the effect of physical effort with increasing intensity on neural activity in the visual pathway in volleyball players (n = 10) and non-athletes (n = 10). Participants performed three 10-min tests of increasing intensity on a cycle ergometer. Each participant was assigned individual workloads below the lactate threshold (40% [Vdot]O(2max)), at the lactate threshold (65-75% [Vdot]O(2max)), and above the lactate threshold (80% [Vdot]O(2max)). Four recordings of visual evoked potentials were made: pre-exercise and immediately after each of the three subsequent tests. We assessed neural activity of the visual pathway by examining the amplitude and latency of the N75, P100, and N135 components of the visual evoked potentials waveform. Pre-exercise P100 wave latency was shorter (P < 0.05) in volleyball players than in non-athletes. In non-athletes, the latency of P100 following the first and second effort (40% and 65-75% [Vdot]O(2max)) was reduced compared with pre-exercise (P < 0.01). However, P100 latency increased and P100 amplitude decreased after the third test (80% [Vdot]O(2max)) in non-athletes. In contrast, no significant changes in the latency or amplitude of visual evoked potentials were observed in the athletes in the three tests. Neural conductivity in the visual pathway after exercise might be at least partially dependent on the individual's personal training adaptation status.
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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) | 0.001 | 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".