Selected perfluorinated compounds in the sediment of an arctic freshwater lake: A case study at Kapp Linnè
Bibliographic record
Abstract
After their discovery in the environment in the early 2000s, polyfluorinated alkylated substances and perfluorinated alkylated substances (PFASs) received much attention because of their persistence, bioaccumulation potential, and possible adverse effects in organisms. Concentrations have been monitored in the biotic and abiotic environment. Due to atmospheric and oceanic transport PFAS have been unambiguously distributed in the environment, also in the Arctic.\n Linnévatnet is a remote lake located on the west coast of Nordenskíold land, Spitsbergen, Svalbard. The aim of this thesis is to determine the levels of selected PFASs in the sediment of Linnévannet with an emphasis on the short chained compounds, and examine some possible point source locations on Svalbard. To examine this, a modified method meant for biota was used. An aim will be to se how the method performs, and if it is suitable for sediment analysis.\n The sampling was carried out at Linnévatnet in June of 2015. Samples where taken with a grab sampler from a rubber boat or from the ice. The samples where transported to the field station and stored in a freezer until transport back to UNIS. At UNIS the samples where dried and packed, then sent to NMBU where the rest of the extraction and clean up were carried out. The extracts where analyzed at campus Adamsstuen, at the Institute for Food Safety and Infection Biology (MatInf, NMBU).\n \n The recoveries where found to be to low, giving the results a significant uncertainty. The results where comparable with other studies conducted at similar sites in the Canadian Arctic. PFCAs and PFOS where found in Linnévatnet at low concentrations. The airport where identified as a possible local source, where high levels of PFSAs and PFCAs where found, suggesting fire fighting foam as a possible source from the associated fire training site.
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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 | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".