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Record W4360843664 · doi:10.22374/jspv.v5i1.22

Survey of Visual and Predictive Aspects of Batting and Eye Care Utilization in Baseball Players

2023· article· en· W4360843664 on OpenAlexvenueno aff
Nick Fogt, Jacob Terry

Bibliographic record

VenueJournal of Sports and Performance Vision · 2023
Typearticle
Languageen
FieldComputer Science
TopicVirtual Reality Applications and Impacts
Canadian institutionsnot available
FundersOhio State UniversityOak Ridge Associated UniversitiesU.S. Department of EnergyOak Ridge Institute for Science and EducationU.S. Department of Defense
KeywordsCoachingPsychologyEye trackingFixation (population genetics)Applied psychologyOptometryMedicineComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Recent laboratory studies suggest that baseball batters use pre-pitch and post-pitch cues in batting and that high-level batters have excellent visual acuity. This study aimed to survey baseball batters on eye and head tracking and fixation behaviors, whether players received eye examinations during their playing careers, and on players’ recollections of coaching advice. An online survey was sent to potential respondents. Fifty-nine current or former baseball players who participated at the college level (54) or above (5) completed all (58) or most of the survey. Most were Division 3 college players. Survey responses suggested that pre-pitch and post-pitch cues were used by batters and that eye and head-tracking behaviors were similar to those in laboratory studies. Survey answers on batters’ behaviors largely matched answers on coaching advice. Most respondents had received an eye examination while playing, but most had not discussed visiontherapy.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.285
Threshold uncertainty score0.178

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.026
GPT teacher head0.315
Teacher spread0.289 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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