Meta-analysis of soil and dust ingestion studies
Bibliographic record
Abstract
The ingestion of soil and dust by children and adults is a potential source of exposure to environmental contaminants. To advance beyond the simple averaging of estimates used in the U.S. EPA's Exposure Factors Handbook (EFH), we describe a novel meta-analysis of all available studies that provided soil or dust ingestion estimates for children or adults conducted in the United States and Canada. Using meta-analytic techniques, we estimate the mean total soil plus dust ingestion rates and confidence intervals (CIs) for eleven age groups (0 - <1 month (m), 1 - <3 m, 3 - <6 m, 6 - <12 m, 1 - <2 years (y), 2 - <3 y, 3 - <6 y, 6 - <11 y, 11 - <16 y, 16 - <21 y, and 21+ y). These age groups were selected for consistency with the EFH update to Chapter 5 and the U.S. EPA's Age Grouping Guidance. For each age group, we calculated best estimates for the three main types of ingestion studies: tracer studies based on the aluminum tracer, biokinetic studies, and activity pattern (modeling) studies, as well as overall estimates for all three study types combined. Our meta-analysis combined study estimates using the alternative statistical approaches of the fixed effect method (inverse variance method, "I-V") and two random effects methods, DerSimonian and Laird's method of moments ("DSL") and the restricted maximum likelihood method ("MIXED"). For each approach, the mean total soil plus dust ingestion rate estimates for each study type generally aligned well with the EFH, ranging from 36 to 68 mg/day for infants, 56-72 mg/day for young children, and 12-32 mg/day for adolescents and adults. When all three study types were combined, the upper bounds of the 95% CI were generally the lowest for the I-V method and the highest for the MIXED method. The estimates produced here can be used for stochastic risk assessments and provide a better estimate of soil and dust ingestion rates across age groups.
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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 | 0.034 | 0.078 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.053 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".