An assessment of hydrodynamic and simulated race performance features of three C-1 hull designs
Bibliographic record
Abstract
Purpose Recently engineered Canadian Single (C-1) canoe hull designs have been found to produce less resistive drag than the traditional Delta design in tow tank test conditions. If these laboratory findings were found to be similar to on-water performance tests, then these new hull designs could give canoe sprint athletes a competitive advantage. However, these claims have not been independently confirmed nor has it been shown that these new designs result in improved performance under race conditions. Three C-1 hull designs (traditional Delta and the recently engineered Armageddon and Ergo-Starlight) were compared in order to detect differences in C-1 boat dynamics. Methods The C-1 canoes were propelled by eleven national- and international-class paddlers who performed 350-m all-out trials from a dead start in each of the three crafts. One-way ANOVA compared differences in means for individual 50-meter segment and 350-meter performance times. Results Performance times over the 350-meter race simulations were significantly faster (p = 0.038) among international-class paddlers with the Armageddon and Ergo-Starlight designs compared with the Delta. Conclusions International level canoeists should expect improved performance times by choosing the Armageddon and Ergo-Starlight versus the Delta-designed C-1.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".