MODEL RESULTS OF KIDNEY BURDENS FROM URANIUM INTAKES
Bibliographic record
Abstract
Uranium is a naturally occurring element, which is both radiologically and chemically toxic. When dealing with intakes of uranium, whether natural or depleted, chemical toxicity to the kidney usually predominates over radiological toxicity. This is especially true for uranium compounds in soluble (inhalation Type F) and moderately soluble (inhalation Type M) forms. To assess chemical toxicity, information on kidney burden per unit intake is required. This study summarizes the kidney burdens per unit intake for common exposures from uranium ingestion and inhalation. ICRP models developed for radiation dosimetry purposes can equally well be used to estimate kidney burdens from uranium intakes. While dosimetric quantities and data are tabulated in ICRP publications, data on uranium burdens in kidney are not explicitly given in these tabulations. In this work, the most recent ICRP models were utilized to generate a compilation of kidney burdens from common intakes. Calculations were made for four age groups from infant to adult. For all age groups, long-term chronic uranium ingestion will result in a kidney burden of 6.6% of daily uranium intake. Comparisons of kidney burdens due to acute ingestion and acute inhalation show that inhaled uranium compounds of Type F and Type M will generally result in higher burdens to kidney compared to the same amount of uranium compounds ingested.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".