Derivation of Biomonitoring Equivalents for aluminium for the interpretation of population-level biomonitoring data
Bibliographic record
Abstract
Aluminium is widely used in many consumer products, however the primary source of aluminium exposure to the Canadian general population is through food. Aluminium can cause neurotoxicity and reproductive toxicity at elevated exposure levels. Health-based exposure guidance values have been established for oral exposure to aluminium, including a Minimal Risk Level (MRL) by the Agency for Toxic Substances and Disease Registry (ATSDR), a Provincial Tolerable Weekly Intake (PTWI) by the Joint FAO/WHO Expert Committee on Food Additives (JECFA) and a Tolerable Weekly Intake (TWI) by the European Food Safety Authority (EFSA). Aluminium concentration in blood and urine can be used as a tool for exposure characterization in a population. A pharmacokinetic (PK) model was developed based on human dosing data to derive blood Biomonitoring Equivalents (BEs), whereas a mass balance approach was used to derive urine BEs for the above guidance values. The BEs for blood for daily intake consistent with the MRL, PTWI and TWI were 18, 16 and 8 μg/L, respectively. BEs for urine for the same guidance values were 137, 123 and 57 μg/L, respectively. The derived BEs may be useful in interpreting population-level biomonitoring data in a health risk context and thereby screening and prioritizing substances for human health risk assessment and risk management.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".