Bibliographic record
Abstract
The purpose of this investigation was to clarify the effects of blade design and oar length on performance in rowing. Biomechanical models and equations of motion were developed to identify the main forces that affect rowing performance. In addition, the mechanical connection between the propelling blade force and the force that the rower applies on the handle was established. On this basis it was found that the blade design and oar dimensions play a significant role on the rowing performance. While rowers have found empirically that larger and/or hydrodynamically more efficient blade shapes need to be rowed with shorter oars, this article explains this tendency from a biomechanical point of view. Based on the presented evidence, it can be concluded that shorter oars will allow rowers to improve the propelling forces without increasing the handle forces. These findings explain tendencies that started with the introduction of new blade shapes in 1991. A 2 x 2 factorial ANOVA was used to test the significance of the oar shortenings that occurred with the introduction of larger blade surfaces while international record times improved during all those years. Consequently, the findings of this investigation encourage coaches to further experiment with shorter oars and oar manufacturers to continue their blade development that would lead to even shorter oars, with the goal of continuous rowing performance improvements.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".