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Record W2330765399 · doi:10.1519/ssc.0000000000000103

The Back Squat

2014· article· en· W2330765399 on OpenAlexaff
Gregory D. Myer, Adam M. Kushner, Jensen L. Brent, Brad J. Schöenfeld, Jason Hugentobler, Rhodri S. Lloyd, Al Vermeil, Donald A. Chu, Jason Harbin, Stuart M. McGill

Bibliographic record

VenueStrength and conditioning journal · 2014
Typearticle
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsUniversity of Waterloo
FundersNational Institute of Arthritis and Musculoskeletal and Skin Diseases
KeywordsSquatPhysical medicine and rehabilitationComputer scienceRisk analysis (engineering)Functional movementIdentification (biology)Physical therapyMedicine

Abstract

fetched live from OpenAlex

Fundamental movement competency is essential for participation in physical activity and for mitigating the risk of injury, which are both key elements of health throughout life. The squat movement pattern is arguably one of the most primal and critical fundamental movements necessary to improve sport performance, to reduce injury risk and to support lifelong physical activity. Based on current evidence, this first (1 of 2) report deconstructs the technical performance of the back squat as a foundation training exercise and presents a novel dynamic screening tool that incorporates identification techniques for functional deficits that limit squat performance and injury resilience. The follow-up report will outline targeted corrective methodology for each of the functional deficits presented in the assessment tool.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.004

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 head0.006
GPT teacher head0.254
Teacher spread0.248 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations235
Published2014
Admission routes1
Has abstractyes

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