Development of Race Profiles for the Performance of a Simulated 2000-m Rowing Race
Bibliographic record
Abstract
The purpose of this study was to determine the race profile for a 2000-m simulated rowing race as well as the effect of training and gender on the race profile. Nineteen men and 19 women undertook a 2000-m simulated rowing race before and after 10 weeks of a typical off-season training program for rowing. Velocity was calculated every 200 m and the deviation in velocities from the mean race velocity (MRV) was plotted every 200 m to produce race profiles for each gender before and after training. The three fastest male rowers varied approximately 0.02 m.s from the MRV after training and displayed a constant-pace model. The fastest female rowers displayed an all-out strategy after training, producing large deviations from MRV. Average squared deviations from the mean (SDM) determined that all groups except the fastest females had a reduction in MRV deviation after training. These results suggest that the optimal race profile for a simulated 2000-m rowing race may be different between genders. Training reduces SDM and influences both male and female pacing patterns such that both exhibit a pacing strategy that is more similar to that of elite athletes in other events of similar and shorter duration.
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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 | 0.005 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".