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Record W3209528033 · doi:10.3390/jfmk6040086

Analysis of Grip Amplitude on Velocity in Paralympic Powerlifting

2021· article· en· W3209528033 on OpenAlexaff
Marcelo Danilllo Matos dos Santos, Felipe J. Aidar, Andres Armas Alejo, Dihogo Gama de Matos, Raphael Fabrício de Souza, Paulo Francisco de Almeida‐Neto, Breno Guilherme de Araújo Tinôco Cabral, Pantelis Τ. Nikolaidis, Beat Knechtle, Filipe Manuel Clemente, Eugenia Murawska‐Ciałowicz, Georgian Bădicu

Bibliographic record

VenueJournal of Functional Morphology and Kinesiology · 2021
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsLift (data mining)MathematicsAmplitudeAthletesFootprintPhysicsCombinatoricsStatisticsMedicinePhysical therapyComputer scienceOpticsGeology

Abstract

fetched live from OpenAlex

(1) Background: Paralympic Powerlifting (PP) is a Paralympic modality that is predominantly about developing maximal force, as there are athletes who lift three times their body weight. Our objective was to evaluate the averages of the velocity for 30% and 50% of 1 Maximum Repetition (1 RM) on different amplitudes of the footprint in PP athletes; (2) Methods: The intervention happened over two weeks, with the first being devoted to the familiarization and testing of 1 RM, while in the second week, through the use of a linear Encoder, tests of velocity average (VA), velocity average propulsive (VAP), and velocity peak (VP) were carried out with loads of 30% and 50% of a maximum repetition 1 RM for 1× of the biacromial distance (BAD) 1.3 × BAD, 1.5 × BAD; (3) Results: There was a significant difference in the average velocity of 1 × BAD (1.16 ± 0.14 m/s, 1.07-1.26 IC; η2p 0.20) when compared to 1.3 × BAD (1.00 ± 0.17 m/s, 0.90-1.09 IC; η2p 0.20) over 30% of 1 RM. For the other velocity variables for 30% and 50% of 1 RM with different grip amplitudes, there were no significant differences; (4) Conclusions: In PP, the 1 × BAD footprint contributes significantly to VA at 30% of 1 RM when compared to the 1.3 × BAD and 1.5 × BAD footprints. For loading at 50% of 1 RM the VA, VAP and VP decreased when compared to 30% of 1 RM, to the extent that the VAP and VP generated with the 1.3 × BAD and 1.5 × BAD footprints were higher than those with 1 × BAD, other than for VA 50% of 1 RM, where the 1 × BAD footprint was superior to the others.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.369

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.032
GPT teacher head0.290
Teacher spread0.258 · 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

Citations11
Published2021
Admission routes1
Has abstractyes

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