MONITORING METHODS AND DOSE ASSESSMENT FOR INTERNAL EXPOSURES INVOLVING MIXED FISSION AND ACTIVATION PRODUCTS CONTAINING ACTINIDES
Bibliographic record
Abstract
Internal dose assessment for intakes of radionuclide mixtures is a difficult task. When the radionuclide mixture contains both the easy to detect gamma emitters, e.g., 60Co and 95Zr, and difficult to detect alpha emitters such as 239Pu and 241Am, a single monitoring method, such as in-vivo counting, is inadequate for detection and dose assessment. Recent experience with task related monitoring for such radionuclide mixtures at Ontario Power Generation CANDU nuclear power plants has offered an opportunity to review this topic and suggest a strategy for monitoring that involves a combination of in-vivo and in-vitro methods. Using the radionuclide composition data in a mixture from an actual case as an example, this paper describes a monitoring strategy for mixed fission and activation products, including the advantages and pitfalls of reliance on surrogate radionuclides for signaling the presence of actinides in the mixture. The described monitoring strategy is consistent with the recommendations of ICRP Publication 78, which advocates a "combination of techniques so as to make the best possible evaluation of an unusual situation, for example, a programme of both body activity and excreta measurements." The use of experience and professional judgement for interpreting the combined in-vivo and in-vitro data for interim and ultimate intake and dose assessment is discussed and emphasized.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".