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Record W2070084164 · doi:10.1519/jsc.0b013e3181ecd359

Mechanical Properties of Weightlifting Bars

2010· article· en· W2070084164 on OpenAlexaff
Loren Z.F. Chiu

Bibliographic record

VenueThe Journal of Strength and Conditioning Research · 2010
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsBar (unit)StiffnessDeformation (meteorology)Structural engineeringBendingBending stiffnessMaterials scienceBending momentHysteresisComposite materialGeologyEngineeringPhysics

Abstract

fetched live from OpenAlex

Weightlifting training and competition involves lifting a revolving shaft bar loaded with weights. The design of a bar and the location of the weights result in bar deformation during lifting tasks. Because there are many manufacturers of weightlifting bars, the actual deformation of a bar may vary, depending on the steel alloys used. A modified 4-point static bending test was used to assess deformation of 8 weightlifting bars and 1 general purpose weight training bar. The apparent stiffness of the bars was determined by plotting bending moment vs. bar deformation (the vertical height difference between the center vs. ends of the bar). All bars tested had an absence of hysteresis during cyclic loading and unloading in 50-kg increments (up to 220-kg total barbell weight), demonstrating pure elastic properties. At maximum loading, bar deformation was 4-5 cm. A large range existed for apparent stiffness. Based on apparent stiffness calculations, recommendations are made for which bars are suitable for weightlifting training and competition. The deformable nature of weightlifting and weight training bars should be considered before their use in exercise, sport, or research.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.055
GPT teacher head0.343
Teacher spread0.288 · 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 designBench or experimental
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

Citations9
Published2010
Admission routes1
Has abstractyes

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