Polyfluoroalkyl compounds in the Canadian Arctic atmosphere
Bibliographic record
Abstract
Environmental context Perfluoroalkyl compounds are of rising environmental concern because of their ubiquitous distribution in remote regions like the Arctic. The present study quantifies these contaminants in the gas and particle phases of the Canadian Arctic atmosphere. The results demonstrate the important role played by gas–particle partitioning in the transport and fate of perfluoroalkyl compounds in the atmosphere. Abstract Polyfluoroalkyl compounds (PFCs) were determined in high-volume air samples during a ship cruise onboard the Canadian Coast Guard Ship Amundsen crossing the Labrador Sea, Hudson Bay and the Beaufort Sea of the Canadian Arctic. Five PFC classes (i.e. perfluoroalkyl carboxylates (PFCAs), polyfluoroalkyl sulfonates (PFSAs), fluorotelomer alcohols (FTOHs), fluorinated sulfonamides (FOSAs), and sulfonamidoethanols (FOSEs)) were analysed separately in the gas phase collected on PUF/XAD-2 sandwiches and in the particle phase on glass-fibre filters (GFFs). The method performance of sampling, extraction and instrumental analysis were compared between two research groups. The FTOHs were the dominant PFCs in the gas phase (20–138 pg m–3), followed by the FOSEs (0.4–23 pg m–3) and FOSAs (0.5–4.7 pg m–3). The PFCAs could only be quantified in the particle phase with low levels (<0.04–0.18 pg m–3). In the particle phase, the dominant PFC class was the FOSEs (0.3–8.6 pg m–3). The particle-associated fraction followed the general trend of: FOSEs (~25 %) > FOSAs (~9 %) > FTOHs (~1 %). Significant positive correlation between ?FOSA concentrations in the gas phase and ambient air temperature indicate that cold Arctic surfaces, such as the sea-ice snowpack and surface seawater could be influencing FOSAs in the atmosphere.
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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 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".