Programmed vs. Thirst-Driven Drinking during Prolonged Cycling in a Warm Environment
Bibliographic record
Abstract
We compared the effect of programmed (PFI) and thirst-driven (TDFI) fluid intake on prolonged cycling performance and exercise associated muscle cramps (EAMC). Eight male endurance athletes (26 ± 6 years) completed two trials consisting of 5 h of cycling at 61% V˙O2peak followed by a 20 km time-trial (TT) in a randomized crossover sequence at 30 °C, 35% relative humidity. EAMC was assessed after the TT with maximal voluntary isometric contractions of the shortened right plantar flexors. Water intake was either programmed to limit body mass loss to 1% (PFI) or consumed based on perceived thirst (TDFI). Body mass loss reached 1.5 ± 1.0% for PFI and 2.5 ± 0.9% for TDFI (p = 0.10). Power output during the 20 km TT was higher (p < 0.05) for PFI (278 ± 41 W) than TDFI (263 ± 39 W), but the total performance time, including the breaks to urinate, was similar (p = 0.48) between conditions. The prevalence of EAMC of the plantar flexors was similar between the drinking conditions. Cyclists competing in the heat for over 5 h may benefit from PFI aiming to limit body mass loss to <2% when a high intensity effort is required in the later phase of the race and when time lost for urination is not a consideration.
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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 | 0.001 |
| 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".