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Record W2259417356 · doi:10.3945/ajcn.115.119339

Higher compared with lower dietary protein during an energy deficit combined with intense exercise promotes greater lean mass gain and fat mass loss: a randomized trial

2016· article· en· W2259417356 on OpenAlexafffund
Thomas M. Longland, Sara Y. Oikawa, Cameron J. Mitchell, Michaela C. Devries, Stuart M. Phillips

Bibliographic record

VenueAmerican Journal of Clinical Nutrition · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
KeywordsLean body massFat massRandomized controlled trialEnergy expenditureWeight gainMedicineAnimal scienceFood scienceBody mass indexInternal medicineChemistryBiologyBody weight

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.002
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: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0040.002
Meta-epidemiology (broad)0.0070.004
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0140.002

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 head0.017
GPT teacher head0.272
Teacher spread0.255 · 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 designRandomized trial
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

Citations251
Published2016
Admission routes2
Has abstractno

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