Prediction of Rowing Functional Threshold Power using Body Mass, Blood Lactate and GxT Peak Power Data
Bibliographic record
Abstract
International Journal of Exercise Science 16(4): 31-41, 2023. Functional Threshold Power (FTP) is a validated index of a maximal quasi steady-state cycling intensity. The central component of the FTP test is a maximal 20-min time-trial effort. A model to predict FTP from a cycling graded exercise test (m-FTP) was published that estimated FTP without the requirement of the exhaustive 20-min time-trial. The predictive model (m-FTP) was trained (developed to find the best combination of weights and bias) on a homogenous group of highly-trained cyclists and triathletes. This investigation appraised the external validity of the m-FTP model vis-à-vis the alternate modality of rowing. The reported m-FTP equation purports to be sensitive to both changing levels of fitness, and exercise capacity. To assess this claim, eighteen (7 female, 11 male) heterogeneously-conditioned rowers were recruited from regional rowing clubs. The first rowing test was a 3-min graded incremental test with a 1-min break between increments. The second test was a rowing adapted FTP test. There were no significant differences between rowing FTP (r-FTP) and m-FTP (230 ± 64 versus 233 ± 60 W, respectively, F = 1.13, P = 0.80). Computed Bland-Altman 95% LoA between r-FTP and m-FTP were (-18 W to + 15 W), sy.x was 7 W, and 95 %CI of regression were 0.97 to 0.99. The r-FTP equation was demonstrated to be effective in predicting a rowers 20-min maximum power; further appraisal of the physiological response to rowing for 60-min at the corresponding calculated FTP requires investigation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".