Prestart Psychophysiological Profile of a 200-m Canoe Athlete: A Comparison of Best and Worst Reaction Times
Bibliographic record
Abstract
The difference between success and failure in 200-m canoe and kayak events is measured in milliseconds. The gold medal for the 200-m kayak in the Summer 2012 Olympic Games in London was won by a margin of 294 milliseconds, and the difference between winning a bronze medal and not reaching the podium was merely 31 milliseconds. In addition to physical fitness, strength, and technique, the ability to focus effectively and manage arousal is crucial to the ability to react quickly off the start. Conversely, the inability to manage arousal and focus has been shown to reduce reaction time (RT) and, in extreme cases, lead to “choking.” Research in sport psychology and psychophysiology has identified multiple psychological, physiological, and neurological characteristics that underlie peak performance. Although many of the skills and characteristics identified in the research are common to most peak performers, it is also well known that each athlete's optimal performance zone for competition is unique. For athletes, identifying these individual zones of optimal physical, psychological, physiological, and neurological functioning can be elusive and difficult to quantify. Existing technology in bio- and neurofeedback presents a unique opportunity for athletes and researchers to explore what individual peak performance looks like both physiologically and neurologically. Thus, the purpose of this case analysis was to explore the psychophysiological differences of a 200-m canoe athlete between his best and worst reaction times.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".