Multiple Methods for Assessing the Dose to Skin Exposed to Radioactive Contamination
Bibliographic record
Abstract
There is the possibility for a worker at a nuclear installation, such as a nuclear power reactor, a fuel production facility or a medical facility, to come in contact with radioactive contaminants. When such an event occurs, the first order of business is to care for the worker by promptly initiating a decontamination process. Usually, the radiation protection personnel performs a G-M pancake probe measurement of the contamination in situ and collects part or all of the radioactive contamination for further laboratory analysis. The health physicist on duty must then perform, using the available information, a skin dose assessment that will go into the worker's permanent dose record. The contamination situations are often complex and the dose assessment can be laborious. This article compares five dose assessment methods that involve analysis, new technologies and new software. The five methods are applied to 13 actual contamination incidents consisting of direct skin contact, contamination on clothing and contamination on clothing in the presence of an air gap between the clothing and the skin. This work shows that, for the cases studied, the methods provided dose estimates that were usually within 12% (1σ) of each other, for those cases where absolute activity information for every radionuclide was available. One method, which relies simply on a G-M pancake probe measurement, appeared to be particularly useful in situations where a contamination sample could not be recovered for laboratory analysis.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".