Deep sea cold seeps are a sink for mercury and source for methylmercury
Bibliographic record
Abstract
Abstract The effect of seafloor cold seeps on the biogeochemical cycling of mercury (Hg) remains enigmatic. Here we demonstrate substantial enrichments of mercury and methylmercury, along with the presence of microbes capable of metabolizing mercury in sediments of the Haima cold seep, South China Sea, by analyzing mercury and methylmercury concentrations, mercury isotopic composition analyses and metagenomic analyses of sediment cores. Compared to the reference area, the sediments in the upper sediment column of the active-seep area were 2.4 times enriched in Hg and 10.5 times in methylmercury. The slope of the capital delta ratio of mercury 199 to mercury 201 (Δ199Hg/Δ201Hg) with 1.23 ± 0.10 in the active-seep area indicate the occurrence of dark redox reactions. Genes related to mercury methylation (hgcA), demethylation (merB) and reduction (merA) were phylogenetically associated with several bacterial and archaeal linages. We roughly estimated an additional 2,835 Mg mercury and 9 Mg methylmercury are stored in cold seep globally. In summary, we propose that cold seeps globally function as a previously unrecognized sink for mercury and source for methylmercury in the deep ocean.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".