Comparison of maximal glycolytic rate from ergometer to on-water sprinting in elite canoe polo players
Bibliographic record
Abstract
As a predictor for anerobic performance in many sports, the maximal glycolytic rate (νLamax) is assessed in a laboratory setting. However, differences between lab-based test and a sport specific field setting remain unclear. The aim of this study was to compare ergometer and on-water tests for νLamax in elite youth canoe polo players. Fifteen elite German youth canoe polo players performed a 15 s all-out sprint on a Dansprint ergometer and a 50 m (males) or 40 m (females) all-out sprint on water. Capillary blood samples were taken before and every minute for 8 min after the sprint to determine ΔLa (difference between resting and peak post-exercise blood lactate concentrations). Body composition was assessed using Tanita BC-601 impedance analysis. Power output during the 15 s all-out ergometer sprint showed a high correlation with fat-free mass ( r = 0.82) and total lactate production ( r = 0.86). A multiple regression model incorporating both parameters improved prediction of power output to 89%. Velocity during the 40 and 50 m on-water sprints correlated moderately with νLamax ( r = 0.72) and body-fat percentage ( r = −0.62). A considerable difference in both ΔLa and νLamax was evident between ergometer and on-water sprinting. νLamax is positively correlated with mean velocity and power output during on-water and ergometer sprinting, and that body composition significantly influences the relationship between lactate accumulation and performance output. Additionally, performance metrics and capillary blood lactate measurements cannot be simply transferred between ergometer and on-water tests, indicating that ergometer-derived values do not reliably predict on-water performance. Highlights: Maximal glycolytic rate is not transferable between ergometer and on-water settings in canoe players. Calculated glycolytic contribution is highly correlated to power output on the ergometer. νLamax is moderately correlated to on-water performance.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".