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Record W4247945508 · doi:10.1123/ijsnem.2018-0020

IOC Consensus Statement: Dietary Supplements and the High-Performance Athlete

2018· article· en· W4247945508 on OpenAlexaff
Ronald J. Maughan, Louise M. Burke, Jiří Dvořák, D. Enette Larson‐Meyer, Peter Peeling, Stuart M. Phillips, Eric S. Rawson, Neil P. Walsh, Ina Garthe, Hans Geyer, Romain Meeusen, Luc J. C. van Loon, Susan M. Shirreffs, Lawrence L. Spriet, Mark Stuart, Alan Vernec, Kevin Currell, Vidya M. Ali, Richard Budgett, Arne Ljungqvist, Margo Mountjoy, Yannis Pitsiladis, Torbjørn Soligard, Uğur Erdener, Lars Engebretsen

Bibliographic record

VenueInternational Journal of Sport Nutrition and Exercise Metabolism · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsWorld Anti-Doping AgencyUniversity of GuelphMcMaster University
Fundersnot available
KeywordsAthletesSports nutritionMisinformationHarmMedicineHealth benefitsBusinessEnvironmental healthPsychologyPhysical therapyPolitical science

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

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)0.041
metaresearch head score (Gemma)0.083
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: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.041
Threshold uncertainty score0.218

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0410.083
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.006
Bibliometrics0.0050.003
Science and technology studies0.0040.004
Scholarly communication0.0080.005
Open science0.0090.008
Research integrity0.0380.022
Insufficient payload (model declined to judge)0.0170.020

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 head0.009
GPT teacher head0.254
Teacher spread0.245 · 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
GenreCommentary

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

Citations517
Published2018
Admission routes1
Has abstractyes

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