Modeling bioaccumulation using characteristic times
Bibliographic record
Abstract
A new formulation of existing mass balance models for bioaccumulation is derived and applied to organisms that respire either water or air. This model employs characteristic time parameters and equations that are mathematically equivalent to those used in existing concentration-rate constant and fugacity models. The equivalence of these traditional formulations and the novel formulation is demonstrated. In all three formulations, the required information includes various physiological and dietary parameters as well as chemical concentrations in food and in the respired medium of water or air. Chemical properties are described by the octanol-water or octanol-air partition coefficient and a metabolic biotransformation half-life. Bioaccumulation, biomagnification, and all uptake and loss rates are expressed using characteristic times that have readily identifiable chemical or biological significance. The ability of the characteristic time formulation to provide an evaluation of the bioenergetic consistency of organism properties is briefly discussed. The model is applied illustratively to a trout as a water-respiring organism and to a wolf as an air-respiring organism, and the results are discussed. It is concluded that the use of characteristic time parameters and equations provides valuable additional insights regarding the relative importance of the various uptake and loss processes and, thus, is complementary to the conventional approaches for modeling bioaccumulation phenomena in a variety of organisms.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".