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Record W4377097233 · doi:10.1016/j.heliyon.2023.e16211

Assessment of asymmetry at different intensities between conventional and paralympic powerlifting athletes

2023· article· en· W4377097233 on OpenAlexaff
Rafael Luiz Mesquita Souza, Felipe J. Aidar, Rodrigo Villar, Gianpiero Greco, Leonardo dos Santos, Luca Poli, Jefferson Lima de Santana, Roberto Carvutto, Dihogo Gama de Matos, Georgian Bădicu, Ana Filipa Silva, Filipe Manuel Clemente, Hadi Nobarı, Francesco Fischetti, Stefania Cataldi

Bibliographic record

VenueHeliyon · 2023
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsUniversity of Manitoba
FundersUniversidade Federal de Sergipe
KeywordsAthletesConcentricEccentricAsymmetrySymmetry (geometry)MathematicsPhysical therapyMedicinePhysicsGeometry

Abstract

fetched live from OpenAlex

Powerlifting competitions require consistent and symmetric lifting of heavy loads and maximal effort, in which, asymmetric lifting results in trial invalidation. Symmetry during this very high intensity movement is determinant to athletes' performance and success in competitions. This study aimed to compare the asymmetry between Conventional Powerlifting athletes (CP) and Paralympic (PP) athletes at intensities of 45 and 80% 1RM before and after a training session. Twenty-two male athletes (11 CP: 29.84 ± 4.21 and 11 PP: 30.81 ± 8.05 years old) participated in this study. Mean Propulsive Velocity (MPV), Maximum Velocity (Vmax) and Power during the concentric and eccentric phases were evaluated at 45%-1RM before and after a training session. For the intensity of 80%-1RM, MPV, Vmax and Power were measured in the first and last series (5 series of 5 repetitions: 5X5) of a training session. PP athletes demonstrated lower velocity and greater symmetry at 45%-1RM, but higher velocity and less asymmetry at 80%-1RM, when compared to CP. The data indicated that PP athletes tend to be slower at lower intensities, faster at higher intensities in absolute values, and have greater symmetry than CP.

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 score0.002
Threshold uncertainty score0.284

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 head0.036
GPT teacher head0.321
Teacher spread0.285 · 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

Citations10
Published2023
Admission routes1
Has abstractyes

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