SU‐E‐T‐306: Radiological Consequences of Atmospheric Releases at PET Isotope Production Facilities
Bibliographic record
Abstract
Purpose: To quantify the radiological consequences of a release of PET isotopes in gaseous form to the environment, in an urban setting. Methods: The worst case scenario is identified heuristically and an analytical treatment of the worst case scenario is made using ideas from turbulent hydrodynamics to calculate the minimum dispersion of a cloud of PET isotopes (11C, 13N, 14O, 15O & 18F) in the atmosphere emerging from a ventilation stack, either from the accelerator vault or from a hot cell. A Gaussian plume model is used to calculate the average radiological consequence. Results: It is shown that 11C production has the worst radiological consequences for an atmospheric release. Whole body and equivalent doses are calculated using Gaussian and uniform radioisotope concentration profiles in the atmosphere for skin, inhalation and external annihilation gamma fields. It is found that the worst case is when there is an atmospheric inversion present, and when the ambient temperature is higher than the stack exhaust temperature as this generates negative buoyancy. The parameters of this model are the stack exhaust velocity, temperature and diameter, the ambient temperature, the released activity, the half‐life, and the mean positron range, but not the release height. It is shown that for the Gaussian plume model the mean wind speed and release height are important as well as the release time. Conclusions: The design features of the ventilation system of a PET facility which can simply and effectively control the radiological consequences of an atmospheric release are identified. The impact of these results on current regulatory thinking on PET isotope production facility design goals and permissible releases in Canada is discussed.
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.
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 | 0.000 | 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".