Shielding Assessment and Optimization of the Target Station for Medical Isotope Production Based on Superconducting Proton Linac
Bibliographic record
Abstract
In response to the worldwide shortage of 99mTc supply caused by the combined consequence from both decommissioning and maintenance of research reactors worldwide, the Institute of Modern Physics (IMP) of the Chinese Academy of Science and Lanzhou University have launched a collaboration to research and develop a 99mTc production solution based on the 25 MeV high-intensity superconducting proton linear accelerator. Radiation from high-current proton bombardment must be evaluated and considered carefully at the design stage to meet radiation protection (RP) policy and requirements of the shielding for the key device. This work employed FLUKA to conduct the shielding assessment of both prompt and residual radiation fields in several iterations, based on a prototype of the high-power target system. The prompt dose rates outside the target station are lower than the institution’s limit. The residual dose rates inside the station fall below 100 μSv/h at 64 h after the end of beam (EOB); the dominant source term is then the target chamber. The service life of the main actuator is expected to be extended by 2.7 times with the current partial shielding design. The simulation accelerating techniques are applied to balance the accuracy of results and the progress of the project at the same time, which is referential to the shielding assessment of large-scale nuclear facilities. The results can also be used in further study and construction of the target station.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".