Tracing Mercury Contamination Sources in Sediments Using Mercury Isotope Compositions
Bibliographic record
Abstract
Mercury (Hg) isotope ratios were determined in two sediment cores collected from two adjacent reservoirs in Guizhou, China, including Hongfeng Reservoir and Baihua Reservoir. Hg isotope compositions were also analyzed in a soil sample collected from the catchment of Hongfeng Reservoir and three cinnabar samples collected from the Wanshan Hg mine. Baihua Reservoir was contaminated with runoff from Guizhou Organic Chemical Plant (GOCP) when metallic Hg was used as a catalyst to produce acetic acid. Hongfeng Reservoir, located upstream of Baihua, receives Hg from runoff and atmospheric deposition. We demonstrated that delta(202)Hg values relative to NIST 3133 of sediment in Baihua Reservoir ranging from -0.60 to -1.10 per thousand were distinctively different from those in Hongfeng Reservoir varying from -1.67 to -2.02 per thousand. While sediments from both Baihua and Hongfeng Reservoirs were characterized by mass dependent variation (MDF), only Hongfeng Reservoir sediments were characterized by mass independent variation (MIF). Moreover, by using a binary mixing model, we demonstrated the major source of Hg in sediment of Hongfeng Reservoir was from runoff due to soil erosion, which was consistent with the conclusion obtained from a previous Hg balance study. This study demonstrates Hg isotope data are valuable tracers for determining Hg contamination sources in 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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".