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Record W1983908679 · doi:10.1016/j.proeng.2010.04.044

Vibration at the wrist and elbow joints during the golf swing reveals shaft-specific swing kinematics

2010· article· en· W1983908679 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

VenueProcedia Engineering · 2010
Typearticle
Languageen
FieldEngineering
TopicSports Dynamics and Biomechanics
Canadian institutionsUniversity of Calgary
FundersHealth Research Board
KeywordsSwingKinematicsVibrationTorqueStructural engineeringEngineeringElbowNoise (video)Computer sciencePhysicsMechanical engineeringAcousticsMedicineAnatomy

Abstract

fetched live from OpenAlex

Current methods of shaft fitting are only partially successful at matching players with optimal equipment. This could be due to player adaptation. Twenty-four players hit drives into a net with clubs of different shaft flexes. This was repeated with vibration applied. Club kinematics were stable across flex conditions with no vibration, and it is probable that players varied the application of torques during the downswing to compensate for changes in club mechanics. With vibration, for eleven players, club head speed and grip speed at impact increased with flex. This suggests these players could not apply desired torques, perhaps due to noise in proprioceptive feedback caused by vibration.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.934
Threshold uncertainty score0.716

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.003
GPT teacher head0.161
Teacher spread0.158 · 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