Networks of Dissolved Organic Matter and Organo-Mineral Associations Stimulate Electron Transfer over Centimeter Distances
Bibliographic record
Abstract
Natural organic matter (NOM) dominated electron transfer has been widely studied in wetlands, freshwater sediments, and peatlands, in which a diffusion-electron hopping mechanism consisting of dissolved organic matter (DOM) and particulate organic matter (POM) was found to mediate electron transfer over centimeter (cm) distances. However, it remains unclear whether such long-distance electron transfer also occurs when NOM is associated with minerals, which form organo-mineral associations (OMAs) and thus are less mobile and accessible. In this study, we investigated the roles of DOM and OMAs in transferring electrons by performing a series of microbial Fe(III)-mineral reduction experiments over a 2 cm distance. We found that significant electron transfer only occurred when both DOM and OMAs were present. Generally, we observed a positive correlation between the relative proportion of DOM and OMAs and the extent of Fe(III) mineral reduction. However, varying the proportion of DOM showed a stronger effect on the Fe(III)-mineral reduction compared to OMAs, indicating that DOM played a more critical role in the electron transfer network. Our findings shed new light on how organic carbon facilitates iron transformation and the associated biogeochemical cycling of nutrients and contaminants in forest soil systems.
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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) | 0.001 | 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".