Bibliographic record
Abstract
The use of liquid crystal stroboscopic sports vision training dates back to our introduction in 1995. With vision being the predominant sensory modality driving a motor response—whether hitting a baseball, shooting a basketball, a goalie stopping an incoming puck hurtling towards him—coaches, sports psychologists, researchers, and sports vision doctors have worked to develop strategies to improve performance. What can be measured for the entire neuro-visual processing? Can you improve this processing? If so, what are the most effective regimens? How long does it last? What is it that the athlete visually discriminates on for meaning and expertise? Saccadic eye movements are the most common visual skill for acquiring a target. When making these eye movements, the brain ‘masks’ the visual world for a few milliseconds until the eye come to a stop. This is known as saccadic omission and suppression. Are there effective strategies to minimize the effect and speed up the saccadic eye movements? Can we improve fixation accuracy? Decrease latencies? Improve target acquisition, sometimes referred to as dynamic visual acuity? A body of research quickly uncovers what stroboscopic vision training (SVT) can and cannot impact. Both anecdotal and statistically significant research studies shed light on SVT enhancing central visual processing, visual concentration, and athletic performance tied to the subskills impacted by SVT.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".