The ergogenic effect of beta-alanine combined with sodium bicarbonate on high-intensity swimming performance
Bibliographic record
Abstract
We investigated the effect of beta-alanine (BA) alone (study A) and in combination with sodium bicarbonate (SB) (study B) on 100- and 200-m swimming performance. In study A, 16 swimmers were assigned to receive either BA (3.2 g·day(-1) for 1 week and 6.4 g·day(-1) for 4 weeks) or placebo (PL; dextrose). At baseline and after 5 weeks of supplementation, 100- and 200-m races were completed. In study B, 14 were assigned to receive either BA (3.2 g·day(-1) for 1 week and 6.4 g·day(-1) for 3 weeks) or PL. Time trials were performed once before and twice after supplementation (with PL and SB), in a crossover fashion, providing 4 conditions: PL-PL, PL-SB, BA-PL, and BA-SB. In study A, BA supplementation improved 100- and 200-m time-trial performance by 2.1% (p = 0.029) and 2.0% (p = 0.0008), respectively. In study B, 200-m time-trial performance improved in all conditions, compared with presupplementation, except the PL-PL condition (PL-SB, +2.3%; BA-PL, +1.5%; BA-SB, +2.13% (p < 0.05)). BA-SB was not different from BA-PL (p = 0.21), but the probability of a positive effect was 78.5%. In the 100-m time-trial, only a within-group effect for SB was observed in the PL-SB (p = 0.022) and BA-SB (p = 0.051) conditions. However, 6 of 7 athletes swam faster after BA supplementation. The probability of BA having a positive effect was 65.2%; when SB was added to BA, the probability was 71.8%. BA and SB supplementation improved 100- and 200-m swimming performance. The coingestion of BA and SB induced a further nonsignificant improvement in performance.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".