Derivation of biomonitoring equivalents (BE values) for zinc
Bibliographic record
Abstract
Bismuth (Bi) is a natural element present in the environmental media. Bismuth has been used medicinally for centuries, specifically for the treatment of gastrointestinal (GI) disorders. Although bismuth toxicity is rare in humans, an outbreak of bismuth-induced neurotoxicity was reported in France and Australia in the mid-1970s. The primary source of bismuth exposure in the general population is via food. US FDA (2019) estimated recommended daily intake (RDI) for bismuth as 848 mg bismuth/day (12.1 mg Bi/kg-d assuming a body weight of 70 kg) for GI tract disorders. Exposures to bismuth can be quantified by measuring concentrations in blood and urine. Biomonitoring equivalents (BEs) were derived based on US FDA's RDI as a tool for interpretation of population-level biomonitoring data. A regression between steady state plasma concentrations and oral intakes was used to derive plasma BEs. A whole blood: plasma partitioning coefficient of 0.6 was used to convert plasma BE into whole blood BE. A mass balance equation with a urinary excretion fraction of 0.0003 was used to derive urinary BE. The BE values associated with US FDA's RDI for plasma, whole blood and urine were 8.0, 4.8 and 0.18 μg/L, respectively. These BE values together with bismuth biomonitoring data may be used in screening and prioritization of health risk assessment of bismuth in the general population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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) | 0.003 | 0.002 |
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".