Metabolic consequences of β-alanine supplementation during exhaustive supramaximal cycling and 4000-m time-trial performance
Bibliographic record
Abstract
The present study investigated the effects of β-alanine supplementation on the resultant blood acidosis, lactate accumulation, and energy provision during supramaximal-intensity cycling, as well as the aerobic and anaerobic contribution to power output during a 4000-m cycling time trial (TT). Seventeen trained cyclists (maximal oxygen uptake = 4.47 ± 0.55 L·min(-1)) were administered 6.4 g of β-alanine (n = 9) or placebo (n = 8) daily for 4 weeks. Participants performed a supramaximal cycling test to exhaustion (equivalent to 120% maximal oxygen uptake) before (PreExh) and after (PostExh) the 4-week supplementation period, as well as an additional postsupplementation supramaximal cycling test identical in duration and power output to PreExh (PostMatch). Anaerobic capacity was quantified and blood pH, lactate, and bicarbonate concentrations were measured pre-, immediately post-, and 5 min postexercise. Subjects also performed a 4000-m cycling TT before and after supplementation while the aerobic and anaerobic contributions to power output were quantified. β-Alanine supplementation increased time to exhaustion (+12.8 ± 8.2 s; P = 0.041) and anaerobic capacity (+1.1 ± 0.7 kJ; P = 0.048) in PostExh compared with PreExh. Performance time in the 4000-m TT was reduced following β-alanine supplementation (-6.3 ± 4.6 s; P = 0.034) and the mean anaerobic power output was likely to be greater (+6.2 ± 4.5 W; P = 0.035). β-Alanine supplementation increased time to exhaustion concomitant with an augmented anaerobic capacity during supramaximal intensity cycling, which was also mirrored by a meaningful increase in the anaerobic contribution to power output during a 4000-m cycling TT, resulting in an enhanced overall 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".