Development of biomonitoring equivalents for barium in urine and plasma for interpreting human biomonitoring data
Bibliographic record
Abstract
The objectives of the present work were: (1) to assemble population-level biomonitoring data to identify the concentrations of urinary and plasma barium across the general population; and (2) to derive biomonitoring equivalents (BEs) for barium in urine and plasma in order to facilitate the interpretation of barium concentrations in the biological matrices. In population level biomonitoring studies, barium has been measured in urine in the U.S. (NHANES study), but no such data on plasma barium levels were identified. The BE values for plasma and urine were derived from U.S. EPA's reference dose (RfD) of 0.2 mg/kg bw/d, based on a lower confidence limit on the benchmark dose (BMDL05) of 63 mg/kg bw/d. The plasma BE (9 μg Ba/L) was derived by regression analysis of the near-steady-state plasma concentrations associated with the administered doses in animals exposed to barium chloride dihydrate in drinking water for 2-years in a NTP study. Using a human urinary excretion fraction of 0.023, a BE for urinary barium (0.19 mg/L or 0.25 mg/g creatinine) was derived for US EPA's RfD. The median and the 95th percentile barium urine concentrations of the general population in U.S. are below the BE determined in this study, indicating that the population exposure to inorganic barium is expected to be below the exposure guidance value of 0.2 mg/kg bw/d.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".