Bibliographic record
Abstract
Most virtual reality systems offer the option of viewing the space using a head mounted display or a head coupled display. These can provide a comfortable way of providing a 3D display while detecting head motion and using that to change the viewing position and angle. However, head mounted displays typically have limited resolution, can create neck and eye strain, and can create user disorientation. Head coupled displays, where the display is usually projected onto a screen and the head mount is used for 3D and orientation only, are now a focus of attention in research and in production systems. They are used in, and in fact have spurred the development of, systems like the CAVE, Immersadesk, and IWall to name just three. However, their use is limited by their high cost, fixed nature, and space requirements, and a focus of research is on making head-coupled displays more easily usable and less expensive. The VirtualWindow project is a simulation of a head-coupled display that can be used to develop software for such systems without the expense of owning one, or at least without using the very expensive space. The simulator uses two webcams to perform 3D head tracking rather than instrumenting the user, and provides a set of useful operations that enhance the development and the viewing experience.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.035 | 0.005 |
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".