A Multivariate Method for Analysing Time and Space Components of Highly Disparate Movement Trajectories
Bibliographic record
Abstract
The difficulties in performing manual control tasks under visual-motor mismatch are well documented. For example, laparoscopic surgeons must operate in an environment where a motor movement and the visual feedback of that same movement are ordinarily misaligned with respect to each other, increasing the risk of control errors. To complement conventional global measures of motor performance, which provide insight into spatial and/or temporal performance averaged over several trials, a bivariate dynamic measure encompassing joint time and space components is proposed. These two components are the instantaneous velocity and movement efficiency, derived from recorded movement trajectories. The dynamic measure consists of a graphical representation of the bivariate data, in conjunction with a non-parametric statistical analysis. An example is given of the dynamic measure applied to movement trajectories from an investigation involving simulated laparoscopic pointing under visual-motor mismatch. Results from both global and dynamic measures are discussed, along with limitations of the multivariate dynamic measure.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".