Predicting the Kinetics of Resupply of Organic Pollutants from Sediments Using Diffusive Gradients in Thin Film Samplers and their Bioavailability to Aquatic Invertebrates
Bibliographic record
Abstract
Abstract The present study used diffusive gradients in thin film (DGT) samplers deployed in situ at a wastewater-impacted site (Clarkboro Ferry) for 20 days to develop a predictive model between time-weighted mean concentrations of seven selected antipsychotic compounds in water and those in resident benthic invertebrates, specifically crayfish (Faxonius virilis). The model was further combined with a model of desorption of antipsychotic compounds to predict kinetics at the sediment–water interface. Antipsychotic compounds were mostly detected in adult crayfish and internal concentrations were similar among targeted compounds, except for lesser concentrations of duloxetine. The model, based on the mass balance of organic chemicals, to predict uptake by organisms exhibited good agreement with measured values (R2 = 0.53−0.88), except for venlafaxine (R2 = 0.35). At the sediment–water interface, positive fluxes were observed for antipsychotic compounds and the results from DGT-induced fluxes in sediments (DIFS) coupled with equilibrium hydroxyl-β-cyclodextrin extraction further indicated partial resupply of antipsychotic compounds from sediments to the aqueous phase, despite the labile pool being relatively limited. The results of the present study affirm that DGT techniques can be used as a predictive tool for contamination in benthic invertebrates and can simulate the ability of contaminant resupply from sediments. Environ Toxicol Chem 2023;42:1696–1708. © 2023 SETAC.
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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 | 0.000 |
| 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) | 0.000 | 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".