A training system based on virtual environments to prevent incidents and reduce accidents during decommissioning of nuclear facilities
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
Decommissioning of nuclear facilities should be accomplished by assuring the safety of workers because these decommissioning activities take place under high radioactivity and difficult work conditions. Before decommissioning, it is necessary to evaluate and assess the radiation exposure dose of workers under the principle of ALARA (as low as reasonably achievable). Furthermore, to improve the proficiency of decommissioning environments, methods and systems need to be developed. The legacy methods of exposure dose measurement and assessment have the limitations to modify and simulate the exposure dose of workers prior to practical activities because those should be accomplished without changes of working routes under predetermined scenarios. To simulate many decommissioning scenarios, decommissioning environments were designed in virtual reality. To simulate and assess exposure dose of workers, a human model was also designed in a virtual environment. These virtual decommissioning environments made it possible to simulate and assess in real time the exposure dose of workers. It can be concluded that this system is able to protect workers from accidents and enable them to improve their familiarization about their working environment. This system is expected to reduce human errors because workers can improve their proficiency of hazardous working environments due to virtual training like real decommissioning situations. In the end, safety during decommissioning of nuclear facilities will be guaranteed under the principle of ALARA.
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 | 0.000 |
| 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