Deterministic and Semiprobabilistic Modeling of the Committed Dose from Radionuclides and the Chemical Burden from Uranium in the New Zealand Diet
Bibliographic record
Abstract
To support New Zealand's food safety monitoring, estimates of the current population exposure to ionizing radiation through diet are needed. To calculate the committed dose from radionuclide activities in the food chain, dietary modeling was undertaken for different age and gender groupings of the New Zealand population. Based on a published survey of radionuclide activity concentrations in the New Zealand diet, deterministic and semiprobabilistic models were constructed to derive estimates of the effective dose via the diet. Deterministic estimated annual doses across the different age and gender groupings ranged from a minimum of 48 to 66 μSv/year for teenage girls to a maximum of 126 to 152 μSv/year for adult males. Polonium-210 was the main contributor to ingested dose, with anthropogenic radionuclides contributing very little. For adults, seafood represented the most important source of exposure, with the contribution from this source decreasing for younger age groups. Results of the semiprobabilistic model identified a range of possible ingested doses, with 2.5 to 97.5th percentile ranges of 0.01 to 1.44 μSv/day for adults and 0.02 to 1.84 μSv/day for children. Estimated doses to the New Zealand population show similarities to those of other countries and fall within the expected global range. The current level of exposure to ionizing radiation in the diet does not represent an elevated health risk.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".