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Record W2186436224 · doi:10.1123/mcj.12.3.210

Visual Feedback Use during a Back Tuck Somersault: Evidence for Optimal Visual Feedback Utilization

2008· article· en· W2186436224 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMotor Control · 2008
Typearticle
Languageen
FieldNeuroscience
TopicMotor Control and Adaptation
Canadian institutionsCanadian Society for Exercise Physiology
Fundersnot available
KeywordsVisual feedbackStability (learning theory)Angular velocityArtificial intelligenceRanking (information retrieval)Computer scienceComputer visionPhysical medicine and rehabilitationPsychologyPhysicsMedicineMachine learning

Abstract

fetched live from OpenAlex

We aimed to determine if visual feedback use during aerial skills is more efficient at low angular head velocity (AHV; i.e., <350 deg/s) than at high AHV. Twelve experienced female acrobats performed 20 back tuck somersaults under four experimental conditions: full-vision (FV), vision at AHV below 350 deg/s (VBelow), vision at AHV above 350 deg/s (VAbove), and no-vision (NV). AHV was calculated in real time, and liquid crystal goggles were used to manipulate vision. Two gymnastics judges scored landing stability using a four-point scale. All vision conditions that allowed some vision yielded significantly better landing scores than in the NV condition. Furthermore, a nonparametric test revealed that VBelow yielded a better performance ranking than the FV condition. We conclude that visual feedback during a back tuck somersault is used for landing stability at all angular head velocities, but optimal feedback use occurs when there is retinal stability.

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.

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)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.923
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.002
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 head0.121
GPT teacher head0.321
Teacher spread0.200 · 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