Bibliographic record
Abstract
The present study is a follow-up of Elliott and Hansen (2010), which compared limb trajectory amendment measures. Because planning mechanisms can influence trajectory scaling and variability between trials, one major weakness of the measures contrasted in Elliott and Hansen (2010) is the use of many limb trajectories to obtain a measure of trajectory amendments (e.g., Heath et al., 2004; Khan et al., 2002). In contrast, a jerk-score can be obtained from a single trial (see Hogan & Flash, 1982). The present study contrasted jerk-score analyses (e.g., Goble et al., 2010) with other limb trajectory amendment measures. Fourteen participants performed reaches to 3 target amplitudes with (V) or without (NV) vision between movement onset and offset. Limb trajectories were monitored using motion tracking and a tri-axial accelerometer. As anticipated, participants exhibited more accurate and precise endpoint distributions in V than NV. Also, analyses using all measures of limb trajectory amendments did exhibit differences between V and NV trials. However, the interaction between the vision and target factors was only significant for the jerk and correlational measures, but not for the trajectory variability measures. Further, the largest partial eta square value for the vision by target interaction was obtained with the Fisher Z-score transformation of the correlational measures. The latter measure may be the most accessible and valid proxy for assessing limb trajectory amendments.Acknowledgments: This research was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC), the Canada Foundation for Innovation (CFI) and the Ontario Research Fund (ORF).
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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 | 0.003 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".