A BIOCHEMICAL-BASED MODEL FOR THE DOSIMETRY OF DIETARY ORGANICALLY BOUND TRITIUM—PART 2: DOSIMETRIC EVALUATION
Bibliographic record
Abstract
In this paper the dosimetry for a novel form of physiological model, whose biokinetics are governed by the overall metabolic reactions of the principal nutrients carbohydrates, fats and proteins, is evaluated by compartmental analysis. Two models of differing complexity, called the HCNO-S and HCNO-C models, were developed from parameters evaluated in an accompanying paper. The simpler form has single compartments representing the principal nutrients. The more complex model includes compartments representing the longer-term retention of carbohydrates as glycogen, fats as adipose tissue, and proteins in bone and soft tissues. The effective doses for various tritiated intakes are the same, or similar, as calculated by the two HCNO models, except for tritiated protein. The dose coefficient for an intake of tritiated water is approximately 8% greater than that recommended by the ICRP when the tritium body burden is considered as a homogenous pool. However, when the composition of individual organs is taken into account, the dose coefficient for an HTO intake is approximately 22% greater than the ICRP value. The HCNO-C dose coefficient for OBT in a normal diet is 5.0 x 10(-11) Sv Bq(-1), which is 1.2-fold greater than the ICRP dose coefficient for an OBT intake. The HCNO-C composition model gave organ and tissue doses with the largest range for a tritiated Reference Man dietary intake, the highest dose (red marrow, then breast) being around three-fold the lowest. A property of the HCNO models, important for bioassay analyses, is that a major part (> 90%) of an OBT intake is oxidized and excreted as HTO, which is physiologically more accurate than the current ICRP OBT model. The effective dose of specific tritiated foods, e.g., rice and wheat, was evaluated on the basis of their constituents.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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 | 0.001 |
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".