High mercury accumulation in deep-ocean hadal sediments
Bibliographic record
Abstract
Abstract Ocean sediments are the largest sink for mercury (Hg) sequestration and hence an important part of the global Hg cycle1. Yet accepted global average Hg flux data for deep-ocean sediments (> 200 m depth) are not based on measurements on sediments but are inferred from sinking particulates2. Mercury fluxes have never been reported from the deepest zone, the hadal (> 6 km depth). Here we report the first measurements of Hg fluxes from two hadal trenches (Atacama and Kermadec) and adjacent abyssal areas (2–6 km). Mercury concentrations of up to 400 ng g−1 were the highest recorded in marine sediments remote from anthropogenic or hydrothermal sources. The two trench systems differed significantly in Hg concentrations and fluxes, but hadal and abyssal areas within each system did not. The relatively low recent mean flux at Kermadec was 6–15 times higher than the inferred deep-ocean average1,3, while the median flux across all cores was 22–56 times higher. Thus, some hadal and abyssal sediments are Hg accumulation hot-spots. The hadal zone comprises only ~ 1% of the deep-ocean area, yet a preliminary estimate based on sediment Hg and particulate organic carbon (POC) fluxes suggests total hadal Hg accumulation may be 12–30% of the estimate for the entire deep-ocean. The few abyssal data show equally high Hg fluxes near trench systems. These results highlight a need for further research into deep-ocean Hg fluxes to better constrain global Hg models.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".