Ethoxyresorufin-<i>O</i>-deethylase induction in trout exposed to mixtures of polycyclic aromatic hydrocarbons
Bibliographic record
Abstract
This study investigated whether ethoxyresorufin-O-deethylase (EROD) activity in rainbow trout exposed to mixtures of polycyclic aromatic hydrocarbons (PAHs) could be predicted from induction equivalency factors (IEF). The test PAHs were classified into strong and weak inducers on the basis of similar exposure-response curves. Induction equivalency factors of strong inducers, based on benzo[k]fluoranthene (BkF) as the reference compound, ranged from 0.03 to 0.16. Trout exposed to mixtures of strong inducers (2, 4, and 6 equipotent parts) at 0.32-, 1.0-, or 3.2-nM BkF-equivalents showed exposure-dependent increases in EROD activity, consistent with an additive interaction. The extent of activity did not vary greatly among mixtures and single PAHs at a given induction equivalent quantity (IEQ). Induction equivalency factors could not be calculated for weak inducers because the range of induction was too low. Hence, each weak inducer was added to mixtures at concentrations that induced EROD activity fivefold. These mixtures appeared additive because binary and quaternary mixtures caused about 10- and 20-fold induction, respectively. Strong inducers mixed the same way also showed additivity. In contrast, EROD induction by mixtures containing both strong and weak PAHs was 800 to 900% greater than expected, suggesting synergistic interactions. Therefore, if mixtures are composed of PAHs that behave similarly, IEFs may be a suitable approach for assessing risk. However, for mixtures that contain PAHs of differing potency and efficacy, bioassays will be a more reliable measure of risk than IEFs.
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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 | 0.001 |
| 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 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".