Multimedia modeling of human exposure to chemical substances: The roles of food web biomagnification and biotransformation
Bibliographic record
Abstract
The Risk Assessment IDentification And Ranking (RAIDAR) model is refined to calculate relative human exposures as expressed by total intake, intake fraction (iF), and total body burden (TBB) metrics. The RAIDAR model is applied to three persistent organic pollutants (POPs) and six petrochemicals using four mode-of-entry emission scenarios to evaluate the effect of metabolic biotransformation estimates on human exposure calculations. When biotransformation rates are assumed to be negligible, daily intake and iFs for the nine substances ranged over six orders of magnitude and TBBs ranged over 10 orders of magnitude. Including biotransformation estimates for fish, birds, and mammals reduced substance-specific daily intake and iF by up to 4.5 orders of magnitude and TBB by more than eight orders of magnitude. The RAIDAR iF calculations are compared to the European Union System for the Evaluation of Substances (EUSES) model iF calculations and differences are discussed, especially the treatment of food web bioaccumulation. Model selection and application assumptions result in different rankings of human exposure potential. These results suggest a need to critically consider model selection and to include reliable biotransformation rate estimates when assessing relative human exposure and ranking substances for priority setting. Recommendations for further model evaluations and revisions are discussed.
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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 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".