Control of Joint Rotations in Overarm Throws of Different Speeds Made by Dominant and Nondominant Arms
Bibliographic record
Abstract
We tested the hypothesis that dominant and nondominant overarm throws of different speeds are made by time-scaling of joint rotations, i.e., by joint rotations that have the same positions and amplitudes but that are scaled in time. Eight skilled subjects stood and made overarm throws with both their dominant and nondominant arms. Six joint rotations were computed from recordings of arm segments made with the search-coil technique. Throws made with nondominant arms were less accurate and had lower ball speeds. In contrast to the hypothesis, dominant arms showed large and consistent differences between fast and slow throws in six-dimensional angular position joint space. These same throws showed similar hand angular paths when these were time-scaled based on ball speed. Nondominant arms showed only small differences in angular position joint space in fast and slow throws. It is concluded that a joint space pattern resembling that predicted by time-scaling occurs in nondominant arm throwing when it is unskilled. However, time-scaling does not occur in dominant arm throwing, i.e., a skilled fast throw is not simply a skilled slow throw whose joint positions and amplitudes remain constant but whose joint velocities are sped-up. We hypothesize for future study that, when subjects first learn to throw at different speeds with their dominant arms, they use time-scaling of joint rotations that involves compensating for interaction torques; then as they become skilled at throwing fast, time-scaling is superseded by a more complex pattern of interjoint coordination that involves exploiting interaction torques.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".