Potentially Active Iron, Sulfur, and Sulfate Reducing Bacteria in Skagerrak and Bothnian Bay Sediments
Bibliographic record
Abstract
In many marine surface sediments, the reduction of manganese (Mn) and iron (Fe) oxides is obscured by sulfate reduction, which is regarded as the predominant anaerobic microbial respiration process. However, many dissimilatory sulfate and sulfur reducing microorganisms are known to utilize alternative electron acceptors such as metal oxides. In this study, we tested whether sulfate and sulfur reducing bacteria are linked to metal oxide reduction based on biogeochemical modeling of porewater concentration profiles of Mn2+ and Fe2+ in Bothnian Bay (BB) and Skagerrak (SK) sediments. Steady-state modeling of Fe2+ and Mn2+ porewater profiles revealed zones of net Fe (0–9 cm BB; ∼10 and 20 cm SK) and Mn (0–5 cm BB; 2–8 cm SK) species transformations. 16S rRNA pyrosequencing analysis of the in-situ community showed that Desulfobacteraceae, Desulfuromonadaceae and Desulfobulbaceae were present in the zone of Fe-reduction of both sediments. Rhodobacteraceae were also detected at high relative abundance in both sediments. BB sediments appeared to harbor a greater diversity of potential Fe-reducers compared to SK. Additionally, when the upper 10 cm of sediment from the SK was incubated with lepidocrocite and acetate, Desulfuromonas was the dominant bacteria. Real-time quantitative polymerase chain reaction (qPCR) results showed decreasing dsrA gene copy numbers with depth coincided with decreased Fe-reduction activity. Our results support the idea that sulfur and sulfate reducing bacteria contribute to Fe-reduction in the upper centimeters of both sediments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".