Nontarget and suspect screening reveals the presence of multiple plastic-related compounds in polar bear, killer whale, narwhal and long-finned pilot whale blubber from East Greenland
Bibliographic record
Abstract
The monitoring of legacy contaminants in sentinel northern marine mammals has revealed some of the highest concentrations globally. However, investigations into the presence of chemicals of emerging Arctic concern (CEACs) and other lesser-known chemicals are rarely conducted, if at all. Here, we used a nontarget/suspect approach to screen for thousands of different chemicals, including many CEACs and plastic-related compounds (PRCs) in blubber/adipose from killer whales (Orcinus orca), narwhals (Monodon monoceros), long-finned pilot whales (Globicephala melas), and polar bears (Ursus maritimus) in East Greenland. 138 compounds were tentatively identified mostly as PRCs, and four were confirmed using authentic standards: di(2-ethylhexyl) phthalate (DEHP), diethyl phthalate (DEP), di(2-propylheptyl) phthalate (DPHP), and one antioxidant (Irganox 1010). Three other PRCs, a nonylphenol isomer, 2,6-di-tert-butylphenol, and dioctyl sebacate, exhibited fragmentation patterns matching those in library databases. While phthalates were only above detection limits in some polar bear and narwhal, Irganox 1010, nonylphenol, and 2,6-di-tert-butylphenol were detected in >50% of all samples. This study represents the first application of a nontarget/suspect screening approach in Arctic cetaceans, leading to the identification of multiple PRCs in their blubber. Further nontarget analyses are warranted to comprehensively characterize the extent of CEAC and PRC contamination within Arctic marine food webs.
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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 | 0.000 |
| 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".