Rowing Performance After Dehydration: An Unexpected Effect of Method
Bibliographic record
Abstract
Purpose: To investigate whether mild dehydration, as a weight reduction strategy for lightweight rowers, compromises rowing performance despite a two-hour rehydration window. Both 2000m time trial and visuomotor performance were assessed for impairment. Methods: Experienced rowers (N=14) twice performed a 2000 m rowing ergometer time trial and visuomotor battery: once euhydrated and once after mild dehydration (-1.68 ± .23% body mass reduction). Weight loss was achieved through a combination of 12-hour (overnight) fluid restriction and sauna exposure. Results: Participants were significantly slower on the 2000 m rowing trial in the dehydration condition than in the euhydration condition (2.44 ± 4.5 s, p<0.05). Hierarchical linear regression analyses revealed that these rowing performance decrements were better accounted for by dehydration achieved overnight through fluid restriction (r2=.504, p<0.01) than by dehydration achieved in the sauna (r2=.025, n.s.). Hierarchical regression also revealed a relationship between dehydration-related rowing performance decrements and dehydration-related changes in visuomotor function (r2=.310, p<0.01). Conclusions: These findings suggest that rowing time-trial performance is negatively affected by relatively small changes in hydration status (<2% body-mass dehydration) and that the method by which dehydration is achieved is important. Performance losses were associated with prolonged fluid abstinence and not with short-term thermal exposure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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".