Shifting microbial communities sustain multiyear iron reduction and methanogenesis in ferruginous sediment incubations
Bibliographic record
Abstract
Abstract Reactive Fe( III ) minerals can influence methane ( CH 4 ) emissions by inhibiting microbial methanogenesis or by stimulating anaerobic CH 4 oxidation. The balance between Fe( III ) reduction, methanogenesis, and CH 4 oxidation in ferruginous Archean and Paleoproterozoic oceans would have controlled CH 4 fluxes to the atmosphere, thereby regulating the capacity for CH 4 to warm the early Earth under the Faint Young Sun. We studied CH 4 and Fe cycling in anoxic incubations of ferruginous sediment from the ancient ocean analogue Lake Matano, Indonesia, over three successive transfers (500 days in total). Iron reduction, methanogenesis, CH 4 oxidation, and microbial taxonomy were monitored in treatments amended with ferrihydrite or goethite. After three dilutions, Fe( III ) reduction persisted only in bottles with ferrihydrite. Enhanced CH 4 production was observed in the presence of goethite, highlighting the potential for reactive Fe( III ) oxides to inhibit methanogenesis. Supplementing the media with hydrogen, nickel and selenium did not stimulate methanogenesis. There was limited evidence for Fe( III )‐dependent CH 4 oxidation, although some incubations displayed CH 4 ‐stimulated Fe( III ) reduction. 16S rRNA profiles continuously changed over the course of enrichment, with ultimate dominance of unclassified members of the order Desulfuromonadales in all treatments. Microbial diversity decreased markedly over the course of incubation, with subtle differences between ferrihydrite and goethite amendments. These results suggest that Fe( III ) oxide mineralogy and availability of electron donors could have led to spatial separation of Fe( III )‐reducing and methanogenic microbial communities in ferruginous marine sediments, potentially explaining the persistence of CH 4 as a greenhouse gas throughout the first half of Earth history.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".