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Record W2968096486 · doi:10.3389/fnut.2019.00139

Editorial: Personalized Sport and Exercise Nutrition

2019· editorial· en· W2968096486 on OpenAlexaff
Bryan Saunders, Ahmed El‐Sohemy, Wim Derave

Bibliographic record

VenueFrontiers in Nutrition · 2019
Typeeditorial
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSports nutritionAthletesMedicinePhysical medicine and rehabilitationComputer sciencePhysical therapy

Abstract

fetched live from OpenAlex

Personalized nutrition is an approach that is currently attracting increasing interest in the area of sport and exercise. The prospect of individually tailored, and therefore more effective, sport and exercise nutrition sounds appealing, but research in this area is still in its infancy. To compliment traditional analysis of group mean response, studies are trying to describe the totality of responses by providing individual performance and health data to an intervention which often show large inter-individual variability. Emerging evidence indicates that sport nutrition strategies may work in some individuals or under certain conditions, yet not in others, likely due to a myriad of environmental and genetic factors, highlighting the necessity in providing a more thorough examination of results. The current Research Topic aimed to provide a platform for original data and reviews on novel strategies for personalized sport and exercise nutrition.

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 categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.129
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0020.001
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 head0.003
GPT teacher head0.222
Teacher spread0.219 · 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.

Study designNot applicable
Domainnot available
GenreEditorial

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
Published2019
Admission routes1
Has abstractyes

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