Stable Pb isotope signals in the Arctic: does the general background exist?
Bibliographic record
Abstract
The crucial historical milestone, phasing out leaded gasoline, has rapidly affected atmospheric Pb's concentration and isotope composition. Distant Arctic localities, often without significant industrial contamination sources, can be influenced by foreign transport. For instance, Greenland is affected by Eurasian and Canadian sources in spring and summer, and North American sources in autumn and winter.Using snow samples, we chose three Arctic/Subarctic localities of Svalbard, Greenland, and Iceland to study the Pb stable isotope signals from the atmosphere. To learn more about possible sources of Pb pollution, we also processed local rock and fuel samples.We filtrated the melted snow to analyze the solid snow particles and the dissolved Pb pool in the snow. The Pb isotope composition in the solid particles was more related to the rock samples in Iceland and Greenland. Signals from rock samples in Greenland are less radiogenic than those we found in Icelandic rocks. In Svalbard, the solid particles are enriched with coal content which is still mined at this locality. In filtrates, the signals from fuel (gasoline/diesel) Pb are present, which indicates that the local sources of car and snowmobile traffic are a significant source of Pb in this area. In Greenland, we also found extremely radiogenic signals in filtrate snow samples. The origin of this source would be more likely related to distant sources by transboundary pollution transfer. From our data, we conclude that several local and distant sources of Pb exist in pristine Arctic and Subarctic localities. Fuel seems to be the predominant source in Nuuk, while other sources, such as coal, are significant in Iceland and Svalbard, even in areas of higher local traffic.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".