Elucidation of the transport and fate of per- and polyfluoroalkyl substances in the high Arctic of Canada.
Bibliographic record
Abstract
Per- and polyfluoroalkyl substances (PFAS) are anthropogenic chemicals that \nwere manufactured since the 1950s. Perfluoroalkyl acids (PFAA) are an important class \nof PFAS and are used for a number of industrial and commercial applications related to \nfluoropolymer manufacturing and surface treatments imparting stain, oil, and water \nrepellency. The detection of PFAS in remote environments, such as the Arctic, where \nthey are neither produced nor used, suggests they undergo long-range transport. It is \nsuggested the long-range transport of PFAS to the Arctic occurs through the ocean, \natmosphere, or some combination of the two. This thesis demonstrates that remote sample \ncollection is an effective strategy for understanding the long-range transport of PFAS to \nthe Arctic of Canada. The analysis of snow, ice, and sediment demonstrates PFAA are \ncontinuously transported to the Arctic of Canada. PFAA deposition is increasing over \ntime in many Arctic regions in Canada, and their occurrence in these environments is \nprimarily attributed to the long-range atmospheric transport and oxidation of volatile \nprecursor chemicals. The results in this thesis support the hypothesis that long-range \natmospheric transport is an important pathway for PFAA to the Arctic, however, they also \nprovide unique insights into the post-depositional transport and fate of PFAS in the \nArctic, especially in environments that are responding to climate warming. These results \ndemonstrate for the first time that climate warming is an important vector for PFAS \ndeposition through the action of enhancing glacier and permafrost ice melting, which \nremobilizes historically archived PFAS in glacier and permafrost ice into recipient \nfreshwater ecosystems in the High Arctic of Canada.
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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 | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".