Development and evaluation of a mechanistic bioconcentration model for ionogenic organic chemicals in fish
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A mechanistic mass balance bioconcentration model is developed and parameterized for ionogenic organic chemicals (IOCs) in fish and evaluated against a compilation of empirical bioconcentration factors (BCFs). The model is subsequently applied to a set of perfluoroalkyl acids. Key aspects of model development include revised methods to estimate the chemical absorption efficiency of IOCs at the respiratory surface (E(W) ) and the use of distribution ratios to characterize the overall sorption capacity of the organism. Membrane-water distribution ratios (D(MW) ) are used to characterize sorption to phospholipids instead of only considering the octanol-water distribution ratio (D(OW) ). Modeled BCFs are well correlated with the observations (e.g., r(2) = 0.68 and 0.75 for organic acids and bases, respectively) and accurate to within a factor of three on average. Model prediction errors appear to be largely the result of uncertainties in the biotransformation rate constant (k(M) ) estimates and the generic approaches for estimating sorption capacity (e.g., D(MW) ). Model performance for the set of perfluoroalkyl acids considered is highly dependent on the input parameters describing hydrophobicity (i.e., log K(OW) of the neutral form). The model applications broadly support the hypothesis that phospholipids contribute substantially to the sorption capacity of fish, particularly for compounds that exhibit a high degree of ionization at biologically relevant pH. Additional empirical data on biotransformation and sorption to phospholipids and subsequent incorporation into property estimation approaches (e.g., k(M) , D(MW) ) are priorities with respect to improving model performance.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it