Toxicokinetic variation in 15 freshwater arthropod species exposed to the insecticide chlorpyrifos
Bibliographic record
Abstract
Recent advances in modeling the processes of the toxicity of chemicals-toxicokinetics (TK) and toxicodynamics (TD)-are improving environmental risk assessment (ERA) through prediction of effects from time-varying exposure. This has been achieved by linking chemical fate and toxicological effects mechanistically, based on internal concentrations, through the tissue residue approach. However, certain questions remain: for example, how do TK and TD differ among species and how does this relate to differences in species sensitivity? In a series of experiments, we studied the TK of [(14)C]chlorpyrifos in 15 freshwater arthropod species, two of which were studied in juvenile and adult life stages. Uptake (k(in)) and elimination (k(out)) rate constants were fitted using a one-compartment single first-order kinetic model. The application of two complementary parameter estimation methods facilitated the calculation of bioconcentration factors (BCF) with prediction intervals and 95% depuration times (t(95)) for all tested species. Extremely slow elimination was observed in some species as well as high overall variation in k(in), k(out), BCF, and t(95) across the tested aquatic arthropod species. This variation has implications for the development of TKTD approaches in ERA, including assessing fluctuating exposure concentrations and the interpretation of observed toxicity responses in the laboratory and in the field.
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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 | 0.000 |
| 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) | 0.000 | 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".