Bibliographic record
Abstract
There are several important inter- and intra-individual variations in individual alpha peak frequency (IAPF) in the cognitive domain. The rationale for the present study was to extend the research on IAPF in the cognitive domain to IAPF in the sport domain. Specifically, the purpose of the present study was twofold: (a) to explore whether baseline IAPF is related to performance in an ice hockey shooting task and (b) to explore whether a shooting task has an effect on IAPF variability. The present investigation is one of the first studies to examine links between IAPF and sport performance. Study results did not show significant changes in IAPF when comparing baseline IAPF and pre- to post-task IAPF across three performance levels. The findings support previous literature in cognitive domain suggesting that IAPF is a stable neurophysiological marker. Future research should consider the following methodological suggestions: (a) measuring IAPF during sport performance instead of at a resting state, (b) changing the pre-performance resting baseline instructions to take into account sport-specific mental preparation, (c) exploring an expert-novice paradigm to accentuate performance ability differences between groups (d) comparing tasks with different levels of complexity, and (e) analyzing the possible correlation between IAPF and performance on different days.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".