On the Use of Location and Occupancy Factors for Estimating External Exposure From Deposited Radionuclides
Bibliographic record
Abstract
Providing a dose estimate for the exposed population is crucial in the case of deposition of a known radioactive material, either through an accident or during routine operations. In the absence of detailed information on each individual, knowing the demographics of the affected population concerning occupational habits and housing allows the determination and use of appropriate location and occupancy factors required for exposure and dose calculations. The previous approach in the United Nations Scientific Committee on the Effects of Atomic Radiation 2013 report, published in 2014, used time-dependent location factors and occupancy factors based on age and occupation. The newly published methodology in the United Nations Scientific Committee on the Effects of Atomic Radiation 2016 report (2017) is simplified, using a single time-independent location factor for indoor occupancy, as well as a single occupancy factor that is independent of the age and occupation of the population considered. In this work the two approaches are compared for different population groups and housing types in the case of both a short-lived and a long-lived radionuclide. It was found that the new simplified methodology, while overestimating the integrated effective dose over 100 y for Cs and Cs, also underestimates the dose on short timescales, especially for the shorter-lived Cs. Additionally, the dose rate is significantly underestimated for certain types of buildings with higher location factors. This was found for both radionuclides in the first year of exposure. In the case of short-lived Cs, the integrated effective dose after 100 y is also underestimated in certain cases. It can be concluded that, while the simplified methodology can reasonably and successfully be applied in cases where dose due to deposition (1) is not the dominant pathway and (2) is part of multistep calculations, caution must be exercised in more complex exposure situations, especially when performing dose assessment in response to an accident.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".