Mercury in the Barents region – River fluxes, sources, and environmental concentrations
Bibliographic record
Abstract
Arctic rivers are receiving increased attention for their contributing of mercury (Hg) to the Arctic Ocean. Despite this, the knowledge on both the terrestrial release sources and the levels of Hg in the rivers are limited. Within the Arctic, the Barents region has a high industrial development, including multiple potential Hg release sources. This study presents the first overview of potential Hg release sources on Norwegian and Russian mainland draining to the Barents Sea. Source categories cover mining and metallurgy industry; historical pulp and paper production; municipal and industrial solid waste handling; fossil fuel combustion; and past military activities. Available data on Hg in freshwater bodies near the identified potential release sources are reviewed. Levels of Hg were occasionally exceeding the national pollution control limits, thereby posing concern to the local human population and wildlife. However, the studies were sparse and often unsystematic. Finally, we present new data of Hg measured in five Barents rivers. These data reveal strong seasonality in the Hg levels, with a total annual flux constituting 2% of the panarctic total. With this new insight we aspire to contribute to the international efforts of reducing Hg pollution, such as through the effective implementation of the Minamata Convention. Future studies documenting Hg in exposed Barents freshwater bodies are warranted.
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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 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".