A physiologically-based pharmacokinetic model for disposition of 2,3,7,8-TCDD in fathead minnow and medaka
Bibliographic record
Abstract
A physiologically-based pharmacokinetic model was developed for the disposition of 2,3,7,8-tetrachlorodibenzo-p-dioxin (2,3,7,8-TCDD) in 2 fish species--fathead minnow and medaka. The model was developed based on the empirical data on disposition of dioxins in fish tissues, as well as existing knowledge on the mechanisms of uptake, distribution, storage, and elimination of dioxins in various species (other than fish). The present study examined the applicability of mechanisms known to occur in other species for fish and concluded that the same mechanisms defined for disposition of 2,3,7,8-TCDD in (mostly) rodents can be applicable for the 2 fish species examined as well. Parameter values for the model were selected and/or calibrated using available databases. Model compartments included the gill, kidney, liver, and other richly-perfused tissues, as well as fat and other slowly-perfused tissues. The model was calibrated using 2 independent datasets for exposure of fathead minnow and medaka to 2,3,7,8-TCDD in water. The initial values of the model parameters were selected from several sources, and calibrated to represent the 2 exposure datasets. With very few exceptions, the estimated parameter values for the 2 species were comparable, and the final predictions were in strong agreement with the observations. The model developed in the present study can therefore be used in the prediction of the body burden of 2,3,7,8-TCDD in fathead minnow and medaka. Uncertainty in the model prediction as a result of variability in input parameters is discussed for the parameters with the highest impacts on the model outcome.
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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 | 0.000 |
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".