Mercury Dynamics in the Sea of Azov: Insights from a Mass Balance Model
Bibliographic record
Abstract
The Sea of Azov, an inland shelf sea bounding Ukraine and Russia, experiences the effects of ongoing and legacy pollution. One of the main contaminants of concern is the heavy metal mercury (Hg), which is emitted from the regional coal industry, former Hg refineries, and the historic use of mercury-containing pesticides. The aquatic biome acts both as a major sink and source in this cycle, thus meriting an examination of its environmental fate. This study collated existing Hg data for the SoA and the adjacent region to estimate current Hg influxes and cycling in the ecosystem. The mercury-specific model "Hg Environmental Ratios Multimedia Ecosystem Sources" (HERMES), originally developed for Canadian freshwater lakes, was used to estimate anthropogenic emissions to the sea and regional atmospheric Hg concentrations. The computed water and sediment concentrations (6.8 ng/L and 55.7 ng/g dw, respectively) approximate the reported literature values. The ongoing military conflict will increase environmental pollution in the region, thus further intensifying the existing (legacy) anthropogenic pressures. The results of this study provide a first insight into the environmental Hg cycle of the Sea of Azov ecosystem and underline the need for further emission control and remediation efforts to safeguard environmental quality.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".