The chemical exposure toxicity space (CETS) model: Displaying exposure time, aqueous and organic concentration, activity, and onset of toxicity
Bibliographic record
Abstract
A 1-compartment toxicokinetic model is used to characterize the chemical exposure toxicity space (CETS), providing a novel graphic tool that can aid in the design of aquatic toxicity tests for fish and for interpreting their results. The graph depicts the solution to the differential equation describing the uptake kinetics of a chemical by a modeled fish under conventional bioassay conditions. The model relates the exposure concentration in the water to a dimensionless time and the onset of toxicity as determined by an estimated or assumed critical body residue or incipient lethal aqueous concentration. These concentration graphs are specific to each chemical and exposure and organism parameters and clearly demonstrate differences in toxicity between chemicals and how factors such as hydrophobicity influence the toxic endpoint. The CETS plots can also be used to assess bioconcentration test conditions to ensure that concentrations are well below toxic levels. Illustrative applications are presented using a recent set of high-quality toxicity data. Conversion of concentrations to chemical activities in the plots enables results for different baseline toxicants to be superimposed. For chemicals that have different modes of toxic action, the increased toxicity then becomes apparent. Implications for design and interpretation of aquatic toxicity tests are discussed. The model, and pictorial visualization of the time-course of aquatic toxicity tests, may contribute to improvements in test design, implementation, and interpretation, and to reduced animal usage. Environ Toxicol Chem 2017;36:1389-1396. © 2016 The Authors. Environmental Toxicology and Chemistry Published by Wiley Periodicals, Inc. on behalf of SETAC.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.007 | 0.002 |
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".