Discussion Panel: Motion Sickness in Virtual Environments
Bibliographic record
Abstract
The purpose of this panel is to provide information on motion sickness in virtual environments and discuss human factors issues associated with visually induced motion sickness. With the continued growth of virtual reality devices comes challenges, one of which is the pervasiveness of motion sickness. A panel of experts on motion sickness will join to discuss how they incite and study sickness in their research, providing lessons on how it can impact other research topics and be avoided in future studies. Panelists use methods such as postural sway, psychophysiological measures, and subjective measures to study different aspects of motion sickness. Technology used by these experts ranges from rotating chairs to high fidelity driving simulators. This panel is oriented for those with simulators who want to know what interventions they can employ to alleviate sickness in their research, those who create virtual environments, and those who use virtual reality devices in their research. Considerations for the design of virtual and augmented reality devices and content will be discussed.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.018 | 0.012 |
| Insufficient payload (model declined to judge) | 0.037 | 0.014 |
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".