Implications of iron minerals in terrestrial anaerobic microbial redox processes for greenhouse and toxic gas emissions, and contaminant dynamics
Bibliographic record
Abstract
Global concerns about increasing emissions of greenhouse gases (GHG), particularly methane (CH4) and nitrous oxide (N2O), and toxic hydrogen sulfide (H2S) gas from terrestrial and aquatic ecosystems warrant a comprehensive understanding of anaerobic microbial redox processes that contribute to these atmospheric emissions. Iron minerals that are widely distributed in natural environments mediate many anaerobic microbial metabolic processes that drive C, N, and S biogeochemical cycles, and create resilience in the terrestrial ecosystem against climate and other environmental changes. In this review, scientific information from recent research is gleaned to provide updated microbial pathways that reveal how Fe minerals, with their different properties and redox speciation, influence microbial redox processes in anaerobic environments (iron-, nitrate- and sulfate-reducing, and methanogenic conditions). These microbial processes have profound positive and negative environmental implications for GHG and H2S emissions in natural environments and also play a vital role in contaminant transformation. This review provides insights into mineral-microbe interactions and the importance of the physicochemical properties of minerals in defining these interactions. Comprehensive knowledge about these processes will help devise strategies to mitigate GHG and H2S emissions and biodegrade organic contaminants in natural and engineered environments.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".