Modeling the Relationship between Velocity and Time to Fatigue in Rowing
Bibliographic record
Abstract
INTRODUCTION: Several mathematical models describe the relationship between velocity and time to fatigue. PURPOSE: The purposes of this study were to evaluate different critical velocity (V(critical)) models applied to rowing ergometry and to investigate prediction of performance time in a 2000-m race based on results from shorter trials. METHODS: Sixteen men performed seven rowing ergometer tests. Velocity and time to fatigue data from the 200-m (approximately 0.5 min) to 1200-m (approximately 3 min) trials and from the 200-m to 2000-m (approximately 6.5 min) trials were fit to a two-parameter hyperbolic model, a three-parameter hyperbolic model, and a three-parameter exponential model. RESULTS: Including data from the 2000-m trial generally resulted in higher R2 and smaller SEE. V(critical) from the three versions of the two-parameter model were 4.71 +/- 0.28 m x s(-1), 4.80 +/- 0.27 m x s(-1), and 5.04 +/- 0.24 m x s(-1) (P < 0.001). The two three-parameter models had high R2 (0.991 and 0.990, respectively) and generated parameter estimates that appeared reasonable. Time for a 2000-m race was predicted better using the two-parameter model (r > 0.974) than using the three-parameter models (r = 0.820-0.870). CONCLUSION: It is necessary to include the relatively long 2000-m predicting trial to describe accurately the velocity-time relationship in rowing. The two-parameter model may be useful in predicting time for a 2000-m race but is not otherwise appropriate for modeling when predicting trials of <1 min duration are included. Choice of model and duration of trials must be considered when using mathematical modeling to derive V(critical) and other parameters in rowing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".