The One Giant Leap commercial wireless power meter can be used for sprint kayaking with the appropriate calibration
Bibliographic record
Abstract
Purpose Two experiments were conducted to determine the construct and concurrent validity of a commercial kayak paddle shaft power meter (OGL) for measuring force and power output in female sprint kayakers. Methods and Results Construct validity: Seven female participants used the same OGL paddle to complete 30 s trials at different stroke rates (60, 80, 100, maximum strokes per minute) while a global positioning system measured kayak velocity. Regression analysis provided a large coefficient of determination (R2≥0.83) between mean power and mean velocity (f(x) = 6.892 × 3). Concurrent validity: Two known weight combinations were used to calibrate the paddle (wide range: 51.5–394.9 N; narrow range: 100.6–247.7 N), whereas both left and right sides of the shaft were statically loaded eight separate times with known weights (51.5 N–394.9 N at 49.1 N intervals) to test its concurrent validity. The right side of the shaft had proportional bias (p < 0.001) and the left side of the shaft had fixed bias (65.7 ± 21.1 N, p = 0.017) when calibrated with a narrow range. Neither shaft side had proportional bias, but both shaft sides had small, fixed biases (left: 18.3 ± 7.4 N, p = 0.043; right: 9.3 ± 3.0 N, p = 0.018) when calibrated with a wide range. Conclusion The study establishes that even though the OGL reports power values that appear to have construct validity up to 4.6 m s-1, calibration with a range of weights that encompasses the projected applied forces is needed to improve the accuracy of the force measurement, and thus the power calculation, by the OGL.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.009 |
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".