Aligning global mercury mitigation with climate action
Bibliographic record
Abstract
Environmental mercury (Hg) pollution affects microbial community structure and functions. Yet, whether and how this influence cascades through microbe-mediated cycling of major greenhouse gases (GHGs) remains poorly understood. This Perspective synthesizes emerging evidence on the Hg-microbe-GHG nexus, exploring the possibility that global Hg emission reductions, while critical for human and planetary health, may cause alterations to microbe-mediated GHG fluxes. Significant knowledge gaps persist, however, regarding the Hg-microbe-GHG nexus, particularly concerning the magnitude and direction of the nexus’s net impact on climate and global environmental change. To bridge these gaps, we propose a three-step roadmap aimed at disentangling the potential impacts of global Hg emission mitigation strategies on microbial communities, associated GHG emissions, and subsequent climate change. Collectively, these joint efforts from scientists, industry, community stakeholders, and policymakers are critical to harmonizing global Hg mitigation efforts with climate action and to ensuring a sustainable future for Earth systems and their inhabitants. The impact of mercury (Hg) mitigation on microbe-mediated greenhouse gas (GHG) dynamics remains elusive. In this Perspective, the authors fill this gap by synthesizing fragmented evidence on the Hg-microbe-GHG nexus, proposing a roadmap to align Hg mitigation with climate action.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".