Canoe paddle resonance characteristics and modelling
Bibliographic record
Abstract
Canoe paddles have been in continuous use for thousands of years. Over that period, the paddle has remained virtually unchanged in basic form and materials. Today, paddles are produced in many different designs by a large number of manufacturers. Prominent within the many factors differentiating the best paddles from the worst is their propensity to resonate during use. This resonance has been so prevalent historically that it has been given its own name: a paddle’s song. The major drawback with paddle resonance is that it is accompanied with the penalty of increased fluid resistance. Paddle manufacturers have long tried to reduce or eliminate paddle resonance, but it is still recognized by paddlers as a significant factor in choosing an optimal paddle. This study examined canoe paddle resonance in laboratory, open water, and computer modelling environments. The goal was to investigate the characteristics of this commonly occurring phenomenon and validate the techniques used in the modelling studies. The hypothesis tested in this paper is that by employing and comparing scientific measurement and modelling techniques it would prove possible to examine the relationship between physical paddle structure, fluid-based excitation, and resonance observed during paddle use. The obtained results indicated that the paddle being studied displayed a number of different characteristic resonance patterns or modes depending on the velocity of water flow over the blade. The results of the computer modelling studies on the resonance modes were found to be in good agreement with the observations made in laboratory and open water experiments.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".