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Record W4200217017 · doi:10.1080/14763141.2021.2011391

Segment power analysis of collegiate softball hitting

2021· article· en· W4200217017 on OpenAlexaff
Nicole M. Bordelon, Kyle W. Wasserberger, Jessica L. Downs Talmage, Kenzie B. Friesen, Jessica Washington, Gretchen D. Oliver

Bibliographic record

VenueSports Biomechanics · 2021
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsTrunkInflowOutflowSwingForearmAccelerationPelvisRotation (mathematics)STRIDETorsoMathematicsGeologyAnatomyPhysicsMechanicsMedicinePhysical medicine and rehabilitationGeometryMeteorologyBiology

Abstract

fetched live from OpenAlex

The primary aim of this investigation was to describe the energy flow through the kinetic chain during softball hitting using a segmental power analysis. Twenty-three NCAA Division I collegiate softball athletes (20.4 ± 1.7 yr; 166.7 ± 22.0 cm; 74.9 ± 15.9 kg) performed three maximum effort swings off a stationary tee placed in the middle of the strike zone. Pelvis, trunk, humerus, forearm and hand segment powers were integrated across four phases of the softball swing (load, stride, acceleration, and follow-through). The load and stride phases had low segment energy inflow and outflow values as well as net segment energy flow for all body segments compared to subsequent phases of the swing. The acceleration phase showed large trunk inflow values relative to the pelvis. There was also descriptively larger front compared to back-side upper extremity inflow. Finally, the follow-through phase showed primarily energy outflow for the upper extremity segments likely attributed to slowing down rotation.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.015
GPT teacher head0.286
Teacher spread0.271 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2021
Admission routes1
Has abstractyes

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