Seeing the Invisible: A VR Approach to Radiation Attenuation Visualization for Nuclear Engineering Laboratory Practices
Bibliographic record
Abstract
The principle of “As Low As Reasonably Achievable” or ALARA is taught through laboratory practices composed of lectures and simulations to maintain the radiation exposure at a minimum. Hands-on practices are limited due to the health risks associated with radioactive exposure, leading to the development of virtual, augmented, and mixed reality simulations that pose no harm to trainees. This article presents the development of a virtual reality (VR) model for attenuating radiation visualization during runtime, employing numerical simulation with VR. Our attenuation model responds dynamically to the environment and does not rely on precalculated radiation fields as other works in the literature. Our approach also includes game elements to enhance the laboratory experience. Our goal is to understand the effects of the virtual environments on usability, engagement, completion time, and radiation dose exposure. Preliminary results indicate that the gamified version was found more engaging as participants felt more competent, less frustrated, and more immersed; it was also perceived as more usable with a SUS score of 81.87/100 in comparison to the nongamified with a SUS score of 58.12/100. Participants were faster when completing the nongamified version with an average of 103.28 ± SD 41.26 s in comparison to 175.31 ± SD 91.16 s with the gamified version. Finally, participants received 2.11 mSv less dose exposure with the nongamified version. We believe that practicing the ALARA principle in VR can offer insights on how trainees approach and work around radiation sources, as not necessarily the faster completion results in less exposure.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".