Bibliographic record
Abstract
There is an increasing interest in analyzing, extracting, and representing human movements in terms of a set of spatial, temporal, and qualitative characteristics for applications such as human-computer interactions and sports and health movement analysis. Information visualization techniques can be used to help people better understand the contents of movements. While all the characteristics of movement may not always be visible or detectable by humans, visualizations can illustrate detailed information about the characteristics of the movement. We present the prototype of an interactive movement analytics framework, called Mova, for feature extraction, feature visualization, and analysis of human movement data. Integrated with a library of feature extraction methods, this platform can be used to anaylze movement qualities and investigate the relationships between its characteristics. In addition, Mova can be used to develop and validate new feature extraction methods with the help of parallel visualization of multiple features. We discuss test-cases in which Mova can be used and detail the road-map for its further development. Link to the platform: http://www.sfu.ca/~oalemi/mova
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.354 | 0.210 |
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".