MODELED CONCENTRATIONS IN RICE AND INGESTION DOSES FROM CHRONIC ATMOSPHERIC RELEASES OF TRITIUM
Bibliographic record
Abstract
The expansion of nuclear power programs in Asia has stimulated interest in the improved modeling of concentrations of tritium in rice, a staple crop grown throughout the far east. Normally, the specific activity model is used to calculate concentrations of tritium in the tissue water of edible plants to assess ingestion dose from chronic releases. However, because rice, like other grains, has much lower water content than most crops, the calculation must also account for organically bound tritium. This paper reviews ways to calculate steady-state concentrations of tritium in rice, including the methods of Canadian and United States regulatory models, and the assumptions behind them. Concentrations in rice and resulting ingestion doses are compared for the various methods, and equations for calculating concentrations are recommended. The regulatory models underestimate doses received from ingestion of rice contaminated with tritium since they do not account explicitly for organically bound tritium. The importance of including organically bound tritium is illustrated in a comparison of doses from rice, leafy vegetables and milk for an Asian diet. Dose factors from tritium for these foods are estimated to be 135, 47, and 20 nSv y(-1)/(Bq m(-3)), respectively. Assuming known air concentrations, tritium concentrations in rice, calculated with the recommended equations, are uncertain by less than a factor 2 when tritium concentrations in the rice paddy water are known, and by less than a factor of 2.3 when concentrations in paddy water are unknown.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".