Manganese-Driven Carbon Oxidation at Oxic–Anoxic Interfaces
Bibliographic record
Abstract
The formation of reactive manganese (Mn) species is emerging as a key regulator of carbon oxidation rates, and thus CO 2 emissions, in soils and sediments. Many subsurface environments are characterized by steep oxygen gradients, forming oxic–anoxic interfaces that enable rapid redox cycling of Mn. Here, we examined the impact of Mn(II) aq oxidation along oxic–anoxic interfaces on carbon oxidation in soils using laboratory-based diffusion reactors. A combination of cyclic voltammetry, X-ray absorption spectroscopy, and X-ray microprobe imaging revealed a tight coupling between Mn(II) aq oxidation and carbon oxidation at the oxic–anoxic interface. Specifically, zones of Mn(II) aq oxidation across the oxic–anoxic transition also exhibited the greatest lignin oxidation potential, carbon solubilization, and oxidation. Microprobe imaging further revealed that the generation of Mn(III)-dominated precipitates coincided with carbon oxidation. Combined, our findings demonstrate that biotic Mn(II) aq oxidation, specifically the formation of Mn(III) species, contributes to carbon oxidation along oxic–anoxic interfaces in soils and sediments. Our results suggest that we should regard carbon oxidation not merely as a function of molecular composition, which insufficiently predicts rates, but in relation to microenvironments favoring the formation of critically important oxidants such as Mn(III).
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".