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Record W4362455595 · doi:10.26603/001c.73311

Alternative Flexibility Training

2023· article· en· W4362455595 on OpenAlexafffund
David G. Behm, José Carlos Aragão-Santos, Negar Korooshfard, Saman Hadjizadeh Anvar

Bibliographic record

VenueInternational Journal of Sports Physical Therapy · 2023
Typearticle
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFlexibility (engineering)MedicineRange of motionStatic stretchingResistance trainingPhysical medicine and rehabilitationMotion (physics)Component (thermodynamics)Range (aeronautics)Physical therapyComputer scienceArtificial intelligenceEngineering

Abstract

fetched live from OpenAlex

Over the last approximately 20 years, research has reported on performance impairments following prolonged durations of static stretching. This has led to a paradigm shift towards dynamic stretching. There has also been a greater emphasis using foam rollers, vibration devices, and other techniques. Recent commentaries and meta-analyses suggest that stretching need not be listed as a fitness component as other activities such as resistance training can provide similar range of motion benefits. The commentary aims to review and compare the effects of static stretching and alternative exercises for improving range of motion.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.960
Threshold uncertainty score0.344

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 head0.067
GPT teacher head0.395
Teacher spread0.328 · 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 designOther design
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

Citations5
Published2023
Admission routes2
Has abstractyes

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