A Physiologically-Based Pharmaco-Kinetic Model for Disposition of Dioxins and Furans in Fish
Bibliographic record
Abstract
A Physiologically Based Pharmacokinetic (PBPK) model was developed for the disposition of dioxins in various fish species. The model was developed based on available information on the mechanisms of uptake, distribution, storage and elimination of dioxins in various species (other than fish) and empirical data on disposition of dioxins in the fish tissues. Two versions of the model were implemented: one for exposure to dioxins in water through the gill and the other one for exposure through food. Model compartments included the gill, kidney, liver and other richly-perfused tissues, as well as fat and other slowly-perfused tissues. In the food exposure version, the gut was also included as a richly-perfused tissue. The water exposure model was calibrated using two independent data sets for exposure of fathead minnow and medaka to 2,3,7,8-TCDD in water. The estimated parameter values in the two data sets were comparable and the predictions agreed with the observations very well. The results were compared to those produced by the default methods (bioconcentration factors). Uncertainty in the model prediction as a result of variability in input parameters was also discussed for the parameters with the highest impacts on the model outcome. The predictions of the food pathway exposure model were compared to data for rainbow trout liver.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".