INFLUENCE OF CRITICAL VELOCITY MODELS AND DISTANCES ON ANAEROBIC WORK CAPACITY PARAMETER ESTIMATES IN ROWERS
Bibliographic record
Abstract
PURPOSE: This study examined the effect of Critical Velocity (CV) models and distances on Anaerobic Work Capacity (AWC) parameter estimates in simulated rowing performance as well the relationship between AWC estimates and selected physiological parameters. METHODS: Sixteen male rowers completed randomised maximal exertion trials (200, 400, 600, 800, 1000, and 1200 m), a maximal oxygen consumption (VO2 max), and an actual 2,000-m simulated rowing race on a Concept II rowing machine. Twelve AWC estimates were obtained using 3 mathematically equivalent CV models and 4 rowing distance combinations. RESULTS: Our study found a significant interaction as well as main effects for both models and distances using a 3 (CV models) X 4 (combination of distances) repeated measures ANOVA. Examination of interactions showed that the long set of distances and model 3 produced the most similar (or stable) AWC estimates. Based on this analysis, the AWC estimate from model 3 long (M3L) was chosen to be correlated with each CV test distance (200m, 400m, 600m, 800m, 1000m, 1200m). No significant correlations were found. In addition, when considering physiological measures, AWC-M3L was found to be only related to relative VO2max (r = −.593, p < 0.05). CONCLUSION: It was concluded that estimates of AWC are greatly influenced by both model and distance sets. M3L was shown to produce the most stable estimate as well it was found that no AWC estimate is a strong predictor of cardiorespiratory fitness in simulated rowing 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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".