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
Gesture and motion evaluation provide an interface for a variety of human-computer interaction (HCI)applications. In particular, using human hand motions as a natural interface tool has motivated an active research area to conduct studies on modeling, analyzing and recognizing various hand motions. Recently, human-computer interaction has been a focus of research in vision-based gesture recognition. In this work, we propose a 3D hand model evaluation method that can recognize soft and elaborate representations of hand motions. The camera views landmarked points on the tips and joints of the fingers in the front plane and estimates the depth of these points using a Soft Kinetic camera [1], an Hidden Markov Model (HMM) is used to evaluate the hand motions. Experimentally, in an effort to evaluate the formation of hand gestures similar to those used in rehabilitation sessions, we studied three evolving motions. Given natural hand features and an uncontrolled environment, we were able to classify and differentiate any unnatural slowness of such motions.
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) | 0.000 | 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 it