A fugacity-based toxicokinetic model for narcotic organic chemicals in fish
Bibliographic record
Abstract
A novel dynamic fugacity-based model is described, developed, and tested that simulates the uptake of narcotic organic chemicals in fish from water as occurs in aquatic bioconcentration and toxicity tests. The physiologically based toxicokinetic model treats the time course of chemical distribution in 4 compartments (tissue groups) in the fish, including the liver, in which biotransformation may occur. In addition to calculating bioconcentration and toxicokinetics, 5 possible toxic endpoints are defined corresponding to chemical concentration, fugacity, or activity reaching a critical value that causes 50% mortality. The mathematical description of multicompartment uptake is simplified by expressing the equations in the fugacity format. The model is parameterized and tested against reported empirical data for the bioconcentration of pentachloroethane in rainbow trout and for uptake and mortality from aquatic exposures to naphthalene and 1,2,4-trichlorobenzene in fathead minnows. Model performance is evaluated, and it is concluded that with suitable parameterization it has potential for application for assessment of both bioconcentration and toxicity expressed as median lethal concentrations, critical body residues, and chemical activity as a function of time to death.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".