METABOLIC COSTS OF ON-WATER AND PADDLING ERGOMETER EXERCISE BOUTS IN DRAGONBOAT PADDLERS
Bibliographic record
Abstract
Recently, there has been a growing reliance on the paddling ergometer as both a training tool as well as a measure of performance for paddling athletes. The purpose of this study was to compare the metabolic costs of a race simulation (high intensity and short duration), completed on a paddling ergometer (E) with the same race simulation completed onûwater (W) in a dragonboat. Nine experienced, female dragonboat paddlers (37.0 ± 2.86 yrs, 164.0 ± 1.96 cm, 63.0 ± 2.35 kg) completed two 2:15 minute race pieces on separate occasions, one on the paddling ergometer and the other in a dragonboat. Preceded by a warm-up, protocol was identical for each subject consisting of a start, two power sections and a finish. Subjects were asked to perform maximally. Minute ventilation, VO2 and VCO2 were monitored using a portable metabolic unit. Heart rate and 3-minute post-exercise lactate were also recorded. Peak oxygen uptake was significantly higher (p < 0.01) on-water (38.67 ± 2.14 ml/min/kg) vs. the ergometer (33.37 ± 1.70 ml/min/kg). No significant differences were found between the ergometer and on-water results for VEmax (E = 101.07 ± 7.00 1/min vs. W = 104.24 ± 8.79 l/min), maximal heart rate (E = 171.00 ± 2.80 bpm vs. W = 174.61 ± 4.03 bpm) or lactate (E = 9.28 ± 1.44 mmol/l vs. W = 7.42 ± 0.80 mmol/l) data. Although, there was a trend towards lower lactate values on-water. These data suggest that the paddling ergometer may be useful in the training and assessment of competitive dragonboat paddlers.
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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) | 0.000 | 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 | 0.000 |
| 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".