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Record W4404754600 · doi:10.3390/nutraceuticals4040037

Beta-Alanine Supplementation for CrossFit® Performance

2024· article· en· W4404754600 on OpenAlexaff
Hannah Verity, Darren G. Candow, Philip D. Chilibeck

Bibliographic record

VenueNutraceuticals · 2024
Typearticle
Languageen
FieldMedicine
TopicBiochemical effects in animals
Canadian institutionsUniversity of ReginaUniversity of Saskatchewan
Fundersnot available
KeywordsBETA (programming language)Computer science

Abstract

fetched live from OpenAlex

This study aimed to investigate whether beta-alanine supplementation (BA) improves performance and rating of perceived exertion (RPE) and reduces the respiratory exchange ratio (RER) during a CrossFit® workout. Fourteen participants were randomized in a double-blind design to either BA or placebo, with 12 participants (7 males, 5 females, 32 ± 9.2 y) completing the study. Participants performed two tests, separated by three weeks of supplementing with either 6.4 g/day of BA or placebo. Performance tests involved time to complete an adapted CrossFit® “Fran” Workout of the Day: 21-15-9 repetition scheme alternating between dumbbell thrusters and kipping pull-ups. No significant differences between the BA group and the placebo group were observed for performance time improvement (−13.4 s vs. −12.9 s, p = 0.97), change in mean RER (0.06 vs. 0.05, p = 0.84), or change in RPE (10-point scale) (−0.4 vs. −0.07, p = 0.56). There was a group × time × time during test interaction for RER (p = 0.021). Compared to pre-testing, post-testing RER was higher at the 25% time point of the test for the BA group and at the 75% and 100% time points in the placebo group (p < 0.05). Beta-alanine did not show significant ergogenic effects during an adapted version of the CrossFit® workout “Fran”, although it might have helped with the buffering of acidity later in the test, based on RER.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.161
Threshold uncertainty score0.852

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.046
GPT teacher head0.389
Teacher spread0.343 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations1
Published2024
Admission routes1
Has abstractyes

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