Bibliographic record
Abstract
This dataset captures head-eye coordination behaviors in Virtual Reality (VR) and Augmented Reality (AR) environments, recording head and gaze movement trajectories across a variety of experimental tasks. Using a Meta Quest Pro headset with eye and head tracking, we collected yaw, pitch, and roll orientations of both the head and gaze at 90Hz from 16 participants. The dataset includes raw recordings from several conditions: (1) a limit test, where participants attended to 17 evenly spaced targets across a -90° to +90° range; (2) a block-stacking task in passthrough mode; (3) a wack-a-mole VR game involving rapid gaze shifts; and (4) a random condition task, where unpredictable gaze targets required spontaneous head-eye movements. For the random condition task, additional annotations provide target positions over time, allowing analysis of how head-eye coordination adapts to dynamic stimuli. This dataset supports research in gaze-based interaction, head-eye coordination modeling, VR ergonomics, and avatar animation.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.036 | 0.076 |
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".