Modeling Uptake of Selected Pharmaceuticals and Personal Care Products into Food Crops from Biosolids-Amended Soil
Bibliographic record
Abstract
Biosolids contain a variety of pharmaceuticals and personal care products (PPCPs). Studies have observed the uptake of PPCPs into plants grown in biosolids-amended soils. This study examined the ability of Dynamic Plant Uptake (DPU) model and Biosolids-amended Soil Level IV (BASL4) model to predict the concentration of eight PPCPs in the tissue of plants grown in biosolids-amended soil under a number of exposure scenarios. Concentrations in edible tissue predicted by the models were compared to concentrations reported in the literature by calculating estimated human daily intake values for both sets of data and comparing them to an acceptable daily intake value. The equilibrium partitioning (EqP) portion of BASL4 overpredicted the concentrations of triclosan, triclocarban, and miconazole in root and shoot tissue by two to three orders of magnitude, while the dynamic carrot root (DCR) portion overpredicted by a single order of magnitude. DPU predicted concentrations of triclosan, triclocarban, miconazole, carbamazepine, and diphenhydramine in plant tissues that were within an order of magnitude of concentrations reported in the literature. The study also found that more empirical data are needed on the uptake of cimetidine, fluoxetine, and gemfibrozil, and other ionizable PPCPs, to confirm the utility of both models. All hazard quotient values calculated from literature data were below 1, with 95.7% of hazard quotient values being below 0.1, indicating that consumption of the chosen PPCPs in plant tissue poses de minimus risk to human health.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".