Examination of the video analysis method for paddling motion with a canoe ergometer
Bibliographic record
Abstract
In canoe sprint, propulsive force is generated when paddling in the water (when resistance is applied to the blade). A canoe ergometer is a machine that reproduces the state when resistance is applied to a blade and utilizes it for practice on land. However, since the canoe ergometer is installed on land, we cannot see the moment when the blade enters the water surface. Thus, it is impossible to confirm the phase in which propulsive force is generated from the video image. Therefore, in this study, we calculated the acceleration data of the shaft edge from the video image of the canoe ergometer during paddling motion and attempted to derive the phase when the propulsive force is generated from the acceleration data. In the analysis, we used video images of paddling with a canoe ergometer in 10 men and 1 woman (5 kayak and 6 canadian subjects) belonging to the university canoe club. The analysis showed that it was possible to clearly derive the phase that produces propulsive force using the shaft edge acceleration data. It was also suggested that the subject’s performance can be quantitatively evaluated using the data of the shaft edge velocity.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 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".