Alteration of iron‐rich lacustrine sediments by dissimilatory iron‐reducing bacteria
Bibliographic record
Abstract
The reduction of Fe during bacterial anaerobic respiration in sediments and soils not only causes the degradation of organic matter but also results in changes in mineralogy and the redistribution of many nutrients and trace metals. Understanding trace metal patterns in sedimentary rocks and predicting the fate of contaminants in the environment requires a detailed understanding of the mechanisms through which they are redistributed during Fe reduction. In this work, lacustrine sediments from Lake Matano in Indonesia were incubated in a minimal media with the dissimilatory iron reducing (DIR) bacterium Shewanella putrefaciens 200R. These sediments were reductively dissolved at rates slower than pure synthetic goethite despite the presence of an 'easily reducible' component, as defined by selective extractions. DIR of the lacustrine sediments resulted in the substrate-dependent production of abundant quantities of extracellular polymeric substances. Trace elements, including Ni, Co, P, Si, and As, were released from the sediments with progressive Fe reduction while Cr was sequestered. Much of the initial trace metal mobility can be attributed to the rapid reduction of a Mn-rich oxyhydroxide phase. The production of organo-Fe(III) reveals that DIR bacteria can generate significant metal complexation capacity. This work demonstrates that DIR induces the release of many elements associated with Fe-Mn oxyhydroxides, despite secondary mineralization.
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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".