Aryl hydrocarbon receptor-mediated potencies in field-deployed plastics vary by type of polymer
Bibliographic record
Abstract
Plastic is able to sorb environmental pollutants from ambient water and might act as a vector for these pollutants to marine organisms. The potential toxicological effects of plastic-sorbed pollutants in marine organisms have not been thoroughly assessed. In this study, organic extracts from four types of plastic deployed for 9 or 12 months in San Diego Bay, California, were examined for their potential to activate the aryl hydrocarbon receptor (AhR) pathway by use of the H4IIE-luc assay. Polycyclic aromatic hydrocarbons (PAH), including the 16 priority PAHs, were quantified. The AhR-mediated potency in the deployed plastic samples, calculated as bio-TEQ values, ranged from 2.7 pg/g in polyethylene terephthalate (PET) to 277 pg/g in low-density polyethylene (LDPE). Concentrations of the sum of 24 PAHs in the deployed samples ranged from 4.6 to 1068 ng/g. By use of relative potency factors (REP), a potency balance between the biological effect (bio-TEQs) and the targeted PAHs (chem-TEQs) was calculated to 24-170%. The study reports, for the first time, in vitro AhR-mediated potencies for different deployed plastics, of which LDPE elicited the greatest concentration of bio-TEQs followed by polypropylene (PP), PET, and polyvinylchloride (PVC).
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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 | 0.000 | 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".