Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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) | 0.001 | 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 it