Evaluation of Near Versus Far Target Visual Focus Strategies With Breaking Putts
Bibliographic record
Abstract
The purpose of this study was to compare near-target (NT) and far-target (FT) visual focus strategies during the stroke on breaking putts. The ball was considered the NT and the FT was the point along the target line closest to the hole. Over three testing session, 28 golfers completed an equal number of putts for each combination of three independent variables: Method (NT, FT), Putt Length (6, 10, 14 ft), and Break (toe-to-heel, heel-to-toe) for a total of 144 putts. The FT method was associated with a significantly higher make percentage (40%) in comparison with the NT (37%), (*p* = .047). There was also a significant Method x Break interaction (*p* = .041); the FT method was relatively more effective for heel-to-toe breaking putts (41%) than the NT method (36%). These findings suggest that a FT visual strategy could be effective for golfers on breaking putts inside 14 ft.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".