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Record W2036763650 · doi:10.1519/jsc.0b013e3181dc4200

Quantification of Rubber and Chain-Based Resistance Modes

2010· article· en· W2036763650 on OpenAlexfundno aff
Daniel T. McMaster, J. Scott Cronin, Michael R. McGuigan

Bibliographic record

VenueThe Journal of Strength and Conditioning Research · 2010
Typearticle
Languageen
FieldMedicine
TopicOrthopedic Surgery and Rehabilitation
Canadian institutionsnot available
FundersEdith Cowan UniversityMcMaster University
KeywordsPolynomialTension (geology)Natural rubberComposite materialMathematicsMaterials scienceStructural engineeringMathematical analysisUltimate tensile strengthEngineering

Abstract

fetched live from OpenAlex

Rubber-based resistance (RBR) bands and standard link steel (SLS) chains are 2 forms of variable resistance used throughout the strength and conditioning and rehabilitation communities. The purpose of this study was to quantify the tension of RBR bands and the mass of SLS chains as a function of displacement (increasing in 10-cm increments). Five sets of RBR bands (14-, 22-, 30-, 48-, and 67-mm widths) and 5 sets of SLS chains (6-, 8-, 10-, 13-, 16-mm diameters) were measured using a load cell and a force plate. The RBR bands exhibited curvilinear tension-deformation relationships and were best represented (R >or= 0.99) by second-order polynomial functions, whereas the SLS chains exhibited linear mass-displacement relationships and were best represented (R = 1) by first-order polynomial functions. There were no significant differences (p > 0.05) in force outputs between the load cell and the force plate testing, although the strain gauge is a relatively cheap and viable method of quantifying the variable resistance of chains and bands. The strength and conditioning practitioner when purchasing bands needs to be aware of resting length differences (3.5-5.2%), which resulted in mean tension imbalances of 8-19% in the same color band. This study provides the strength and conditioning coach and clinician with a methodology to quantify variable resistance, which may be useful in the prescription of specific loading intensities.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.651
Threshold uncertainty score0.173

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
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 head0.037
GPT teacher head0.351
Teacher spread0.314 · 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 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

Citations40
Published2010
Admission routes1
Has abstractyes

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