Metabolic potential of microbial communities from ferruginous sediments
Bibliographic record
Abstract
-poor) conditions are generally restricted to freshwater sediments on Earth today, but were likely widespread during the Archean and Proterozoic Eons. Lake Towuti, Indonesia, is a large ferruginous lake that likely hosts geochemical processes analogous to those that operated in the ferruginous Archean ocean. The metabolic potential of microbial communities and related biogeochemical cycling under such conditions remain largely unknown. We combined geochemical measurements (pore water chemistry, sulfate reduction rates) with metagenomics to link metabolic potential with geochemical processes in the upper 50 cm of sediment. Microbial diversity and quantities of genes for dissimilatory sulfate reduction (dsrAB) and methanogenesis (mcrA) decrease with increasing depth, as do rates of potential sulfate reduction. The presence of taxa affiliated with known iron- and sulfate-reducers implies potential use of ferric iron and sulfate as electron acceptors. Pore-water concentrations of acetate imply active production through fermentation. Fermentation likely provides substrates for respiration with iron and sulfate as electron donors and for methanogens that were detected throughout the core. The presence of ANME-1 16S and mcrA genes suggests potential for anaerobic methane oxidation. Overall our data suggest that microbial community metabolism in anoxic ferruginous sediments support coupled Fe, S and C biogeochemical cycling.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".