Perfluorinated Chemicals in Sediments, Lichens, and Seabirds from the Antarctic Peninsula — Environmental Assessment and Management Perspectives
Bibliographic record
Abstract
Antarctica is one of the last frontiers of the planet to be investigated for the environmental transport and accumulation of persistent organic pollutants. Perfluorinated contaminants (PFCs) are a group of widely used anthropogenic substances, representing a significant risk to wildlife and humans due to their high biomagnification potential and toxicity risks, especially in food webs of the northern hemisphere and Arctic. Because the assessment of PFCs in the Antarctic continent is scarce, questions linger about the long-range transport and bioaccumulation capacity of PFCs in Antarctic food webs. To better understand the global environmental fate of PFCs, sediment, lichen (Usnea aurantiaco-atra), and seabird samples (southern giant petrel, Macronectes giganteus; gentoo penguin, Pygoscelis papua) were collected around the Antarctic Peninsula in 2009. PFC analytes were analyzed by LC/MS/MS, revealing the detection of PFHpA in seabirds’ feather and fecal samples, and PFHxS in lichens. PFBA and PFPeA were detected in 80% and 60% of the lichens, and PFTA in 60% of sediment samples. While oceanic currents and atmospheric transport of PFCs may explain the ubiquitous nature of these contaminants in the Antarctic Peninsula, military bases and research stations established there may also be contributing as secondary sources of PFCs in the Antarctic ecosystem.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".