Bibliographic record
Abstract
Leg drive is encouraged in paddling to increase power per stroke. When involving additional muscle mass during exercise, it can be expected that the metabolic demand would also increase. Treadmill (TM) and paddling ergometer (PE) with leg drive, (PEL) and with no leg drive (PENL) incremental testing to fatigue was recorded in 22 healthy male subjects. Eleven experienced outrigger canoeists (P) (age=35.64±5.66 yrs, ht=179.16±3.81 cm, wt=84.39±9.23 kg) and eleven matched controls (C) (age=36.45±5.66 yrs, ht=178.85±4.07 cm, wt=83.95±8.32 kg) participated. Metabolic variables were monitored using the COSMED K4b² portable metabolic system. Oxygen consumption was significantly increased with the addition of leg drive during maximal exercise on the paddling ergometer (PEL=3.88±0.53, PENL=3.23±0.47 L/min). Paddlers attained a higher percentage (14.58% higher than controls) of treadmill VO2max when using leg drive. Furthermore, trained individuals (the paddling group) were able to reach higher percentages of treadmill VO2max during paddling tests both with legs (P=85.05±7.82 vs. C=67.52±4.58) and without legs (P=70.47±5.47 vs. C=61.79±4.16) when compared to the untrained individuals. There was no significant difference between oxyhaemoglobin saturation levels of paddlers and controls across the three testing conditions. Ventilatory thresholds were significantly higher on the TM than on the PE, but were not significantly different between groups. At exercise intensities of 75% and 100% VO2max during PEL significantly higher breathing frequency (45.57±6.86 vs. 57.71±7.99 br/min respectively), tidal volume (2.20±0.35 vs. 2.57±0.35 L respectively), and minute ventilation (99.44+20.17 vs. 146.84±18.54 L/min respectively) values were recorded in paddlers. In addition, at the same intensities of 75% and 100% VO2max, stroke rate was significantly correlated with breathing frequency (r=0.833, r=0.693 respectively), indicating entrainment in the paddling group. Therefore leg drive does appear to affect the energy cost of paddling. These results suggest that the metabolic demand of exercise is sensitive to the specificity of testing conditions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".