Predicting Polycyclic Aromatic Hydrocarbon Bioavailability to Mammals from Incidentally Ingested Soils Using Partitioning and Fugacity
Bibliographic record
Abstract
Soil and dust ingestion is one of the major human exposure pathways to contaminated soil; however, pollutant transfer from ingested substances to humans cannot currently be confidently predicted. Soil polycyclic aromatic hydrocarbon (PAH) bioavailability is likely dependent upon properties linked to chemical potential and partitioning such as fugacity, fugacity capacity, soil organic carbon, and partitioning to simulated intestinal fluids. We estimated the oral PAH bioavailability of 19 historically contaminated soils fed to juvenile swine. Between soils, PAH blood content, with the exception of benzo(a)pyrene, was not linked to fugacity. In contrast, between individual PAHs, using partitioning explained PAH blood content (area under the curve = 0.47 log fugacity + 0.34, r(2) = 0.68, p < 0.005, n = 14). Soil fugacity capacity predicts PAH soil concentration with an average slope of 0.30 (μg PAH g(-1) soil) Pa(-1) and r(2)'s of 0.61-0.73. Because PAH blood content was independent of soil concentration, soil fugacity correlated to PAH bioavailability via soil fugacity's link to soil concentration. In conclusion, we can use fugacity to explain PAH uptake from a soil into blood. However, something other than partitioning is critical to explain the differences in PAH uptake into blood between soils.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".