Identification of Mid-Polar and Polar AhR Agonists in Cetaceans from Korean Coastal Waters: Application of Effect-Directed Analysis with Full-Scan Screening
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Major aryl hydrocarbon receptor (AhR) agonists were identified in extracts of blubber, liver, and muscle from six long-beaked common dolphins ( Delphinus capensis ) and one fin whale ( Balaenoptera physalus ) collected from Korean coastal waters using effect-directed analysis. Results of the H4IIE- luc bioassay indicated that the polar fractions of blubber and liver extracts from the fin whale exhibited relatively high AhR-mediated potencies. Based on full-scan screening with high-resolution mass spectrometry, 37 AhR agonist candidates, spanning four use categories: pharmaceuticals, pesticides, cosmetics, and natural products, were selected. Among these, five polar AhR agonists were newly identified through toxicological confirmation. Concentrations of polar AhR agonists in cetaceans were tissue-specific, with extracts of blubber and liver containing greater concentrations than muscle extracts. Polar AhR agonists with great log K OA values (>5) were found to biomagnify in the marine food chain potentially. Polar AhR agonists contributed 8.9% of the observed AhR-mediated potencies in blubber and 49% in liver. Rutaecarpine and alantolactone contributed significantly to the total AhR-mediated potencies of blubber, whereas hydrocortisone was a major AhR contributor in the liver of the fin whale. This study is the first to identify the tissue-specific accumulation of polar AhR agonists in blubber and liver extracts of cetaceans.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".