Eurasian river spring flood observations support net Arctic Ocean mercury export to the atmosphere and Atlantic Ocean
Bibliographic record
Abstract
Midlatitude anthropogenic mercury (Hg) emissions and discharge reach the Arctic Ocean (AO) by atmospheric and oceanic transport. Recent studies suggest that Arctic river Hg inputs have been a potentially overlooked source of Hg to the AO. Observations on Hg in Eurasian rivers, which represent 80% of freshwater inputs to the AO, are quasi-inexistent, however, putting firm understanding of the Arctic Hg cycle on hold. Here, we present comprehensive seasonal observations on dissolved Hg (DHg) and particulate Hg (PHg) concentrations and fluxes for two large Eurasian rivers, the Yenisei and the Severnaya Dvina. We find large DHg and PHg fluxes during the spring flood, followed by a second pulse during the fall flood. We observe well-defined water vs. Hg runoff relationships for Eurasian and North American Hg fluxes to the AO and for Canadian Hg fluxes into the larger Hudson Bay area. Extrapolation to pan-Arctic rivers and watersheds gives a total Hg river flux to the AO of 44 ± 4 Mg per year (1σ), in agreement with the recent model-based estimates of 16 to 46 Mg per year and Hg/dissolved organic carbon (DOC) observation-based estimate of 50 Mg per year. The river Hg budget, together with recent observations on tundra Hg uptake and AO Hg dynamics, provide a consistent view of the Arctic Hg cycle in which continental ecosystems traffic anthropogenic Hg emissions to the AO via rivers, and the AO exports Hg to the atmosphere, to the Atlantic Ocean, and to AO marine sediments.
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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 | 0.000 | 0.001 |
| 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".