Metal-driven anaerobic oxidation of methane and the Sturtian deglaciation
Bibliographic record
Abstract
The Sturtian and Marinoan glaciations shaped Neoproterozoic palaeoenvironmental evolution. While methane emission likely intensified the Marinoan greenhouse effect, its role during the Sturtian glaciation—coinciding with widespread iron formations (IFs)—remains poorly understood. Here, we analysed bio-essential metals (Ni, Co, Zn), rare earth elements and yttrium (REY), Fe (δ56Fe) and Ni (δ60Ni) isotopes in hematite and magnetite, alongside bulk-rock and in-situ C isotopes of Mn-rich carbonates from five well-preserved Sturtian-aged IFs in South China. Our findings provide geochemical evidence for a methane-related biogeochemical pathway driving Fe-bearing mineral transformation via methanogenesis and metal-driven anaerobic methane oxidation (AOM), mediated by methanogens and anaerobic methane-oxidizing archaea (ANME) in ferruginous settings. Additionally, the Sturtian deglaciation facilitated atmospheric-oceanic O2 exchange, increased nutrient influx from weathering, and methane release under slow AOM oxidation kinetics, potentially aiding ice sheet melting or prolonging glacial waning. Metal-driven anaerobic oxidation of methane and methanogenesis, mediated by a diverse microbial community, drove iron-mineral transformation and may have aided ice sheet melting, according to analysis of Sturtian iron formations from South China
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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) | 0.001 | 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".