Bibliographic record
Abstract
Virtual reality is a rapidly expanding technology which places users in an immersive and interactable virtual environment. To this day, VR technology has been implemented in an array of applications. One such application is medical visualization. To evaluate such an application, SlicerVR was developed. SlicerVR is a virtual reality extension of 3D Slicer, an open-source medical image analysis and visualization platform. By extending 3D Slicer, SlicerVR will have easy access to a wide variety of tools used for medical visualization. SlicerVR was designed to be a flexible and extensible basis for virtual reality technology in the medical field, for use by medical professionals, researchers, students. With this in mind, we designed SlicerVR with intuitive controls for ease of use, progressive rendering to manage issues of motion sickness, and a variety of customizable settings to optimize an individual’s experience. To test SlicerVR, we designed an experiment that requires participants to complete timed-tasks while traversing complex virtual environments. With our experiment we can asses the advantages of stereoscopic vision in comprehending complex anatomical structures as well as the feasibility of navigating said scenes.
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.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".