Combining biomonitoring data in children and biokinetic modeling to guide decision-making for health risk management – a case study on lead emitted by a smelter in Rouyn-Noranda, Canada
Bibliographic record
Abstract
This work aims at illustrating the benefit of combining lead (Pb) biomonitoring data with toxicokinetic simulations in order to confidently identify the risk management intervention that favors the greatest reduction of blood Pb level (BLL) in children exposed to smelter emissions in a mining city from northern Quebec, Canada. The U.S. EPA’s Integrated Exposure and Uptake BioKinetic (IEUBK) model was parametrized with relevant environmental concentrations data to simulate background BLL (in average Canadian children) and local BLL (in children from the concerned community). The resulting simulations were compared with corresponding BLL biomonitoring data. Next, soil and air concentrations were lowered sequentially within the IEUBK model to values specified in Quebec’s environmental regulations. IEUBK simulations predicted mean BLL values that were similar to the measured biomonitoring values, for both the background (predicted: 0.56 vs observed: 0.5 µg/dL) and local BLL (1.24 vs 1.16 µg/dL). Repeating local BLL simulations with lower Pb concentration in the air or soil based on regulatory guidelines showed a much stronger impact of decreasing soil Pb as compared to air Pb. Concluding, this work shows that soil remediation should be prioritized to lower local children’s BLL. Also, the combined use of case-specific environmental Pb levels and biomonitoring data can increase the level of confidence toward an IEUBK-driven identification of the most effective measure for lowering children’s BLLs, in the ≤2 µg/dL domain.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".