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Record W2951358990 · doi:10.1136/bjsports-2019-100990

Infographic. The effect of protein supplementation on resistance training-induced gains in muscle mass and strength

2019· review· en· W2951358990 on OpenAlexaff
Robert W. Morton, Kevin T. Murphy, Sean McKellar, Brad J. Schöenfeld, Menno Henselmans, Eric R. Helms, Alan A. Aragon, Michaela C. Devries, Laura Banfield, JAMES KRIEGER, Stuart M. Phillips

Bibliographic record

VenueBritish Journal of Sports Medicine · 2019
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsUniversity of WaterlooMcMaster University
Fundersnot available
KeywordsResistance trainingInfographicMuscle massMuscle strengthStrength trainingTraining (meteorology)MedicinePhysical therapyMuscle proteinPhysical medicine and rehabilitationEndocrinologyFood scienceBiologyComputer scienceSkeletal musclePhysicsData mining

Abstract

fetched live from OpenAlex

When you lift weights you get stronger and your muscles can get bigger, a process we call hypertrophy, and these changes can mean a big advantage in certain sports. We all ‘know’ that we need to consume supplemental protein when we lift weights to get bigger muscles, right? But what’s the real (science-based) answer? A meta-analysis is a way of looking at all of the studies that have been done in a particular area of science. In our study,1 we performed …

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.071
Threshold uncertainty score0.237

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0710.018

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 head0.026
GPT teacher head0.310
Teacher spread0.284 · 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 designSystematic review
Domainnot available
GenreReview

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
Published2019
Admission routes1
Has abstractyes

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