β-alanine efficacy for sports performance improvement: from science to practice
Bibliographic record
Abstract
β-alanine is a popular supplement among athletes with 61% of competitive team sport players recently surveyed reporting β-alanine use.1 Despite its popularity, there is limited evidence on the most effective supplementation strategy and the systematic review and meta-analysis published by Sauders B et al 2 has shed some light on this issue. Athletes' understanding of β-alanine potential benefits and appropriate daily dose and duration of consumption is low,1 potentially compromising the impact of β-alanine supplementation in a real world setting. This editorial aims to highlight issues regarding the efficacy of β-alanine supplementation and suggest possible approaches to improve its effectiveness in the field. The mechanism of ergogenic effect of β-alanine as the precursor to carnosine synthesis is associated with an expansion of its key physiological role as a proton buffer with potential for antioxidant, glycation and calcium regulation influence.3 Increases in carnosine muscle levels depend on the β-alanine load provided.4 β-alanine supplementation of 4–6 g/day for …
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".