Rowing Performance After Dehydration: An Effect of Method
Bibliographic record
Abstract
Purpose: To investigate whether mild hypohydration in lightweight rowers compromises rowing performance despite a two-hour rehydration window. Methods: Experienced varsity rowers [11 male (82.3 ± 26.2 kg, age = 21.3 ± 4.0 years, height = 184.7 ± 2.4 cm) and three female (62.1 ± 11.8 kg, 2.3 ± 4.2 years, 166.4 ± 16.2 cm)] performed a 2000 m rowing ergometer time trial and visuomotor battery twice: once euhydrated and once after mild dehydration. Weight loss (−1.68 ± 0.23% body mass reduction) was achieved through a combination of 12 h (abstinence) of fluid and food restriction and sauna exposure. Results: Participants were significantly slower on the 2000 m rowing trial in the hypohydration condition than in the euhydration condition (+2.44 ± 4.5 s, p < 0.05). Hierarchical linear regression analyses revealed that this rowing performance decrement was explained by hypohydration achieved overnight through fluid abstinence (r2 = 0.504, p < 0.01) but not by hypohydration achieved in the sauna (r2 = 0.025, n.s.), corroborating our previous finding. This analysis also revealed a relationship between hypohydration-related rowing performance decrements and hypohydration-related changes in visuomotor function (r2 = 0.310, p < 0.01). Conclusions: These findings suggest that rowing time trial performance can be negatively affected by relatively small changes in hydration status and that the method by which hypohydration is achieved is important. Rowing performance losses were explained by hypohydration due to prolonged fluid abstinence and by hypohydration-related changes to neural control of movement. Performance losses were not related to rapid sauna-based fluid loss.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".