Seeing the Invisible: A VR Approach to Radiation Attenuation Visualization for Nuclear Engineering Laboratory Practices
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it