AhR-mediated activities of polycyclic aromatic compound (PAC) mixtures are predictable by the concept of concentration addition
Bibliographic record
Abstract
Risk assessments of polycyclic aromatic hydrocarbons (PAHs) are complicated because these compounds exist in the environment as complex mixtures of hundreds of individual PAHs and other related polycyclic aromatic compounds (PACs). In this study, the hypothesis that concentration addition (CA) can be used to predict the aryl hydrocarbon receptor (AhR)-mediated activity of PACs in mixtures containing various combinations of PACs was tested. AhR-mediated activities of 18 mixtures composed of two to 23 PACs, which included PAHs, azaarenes and oxygenated PAHs, were examined by the use of the AhR-based H4IIE-luc bioassay. Since greater AhR-mediated activities have been observed in soils contaminated by PAHs, investigations were done to test whether soil extract matrix or the presence of non-effect PACs might affect responses of the H4IIE-luc bioassay. Our results showed that AhR-mediated activities of mixtures of PACs could be predicted by the use of concentration addition. Additive activities of PACs in multi component mixtures along with the insignificant effect of the soil matrix support the use of concentration addition in mass balance calculations and AhR-based bioassays in risk assessment of environmental samples. However, independent action (IA) could not be used to predict the activity of mixtures of PACs.
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How this classification was reachedexpand
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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 teacher head, 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".