Bacterial diversity in Fe‐rich hydrothermal sediments at two South Tonga Arc submarine volcanoes
Bibliographic record
Abstract
Seafloor iron oxide deposits are a common feature of submarine hydrothermal systems. Morphological study of these deposits has led investigators to suggest a microbiological role in their formation, through the oxidation of reduced Fe in hydrothermal fluids. Fe-oxidizing bacteria, including the recently described Zetaproteobacteria, have been isolated from a few of these deposits but generally little is known about the microbial diversity associated with this habitat. In this study, we characterized bacterial diversity in two Fe oxide samples collected on the seafloor of Volcanoes 1 and 19 on the South Tonga Arc. We were particularly interested in confirming the presence of Zetaproteobacteria at these two sites and in documenting the diversity of groups other than Fe oxidizers. Our results (small subunit rRNA gene sequence data) showed a surprisingly high bacterial diversity, with 150 operational taxonomic units belonging to 19 distinct taxonomic groups. Both samples were dominated by Zetaproteobacteria Fe oxidizers. This group was most abundant at Volcano 1, where sediments were richer in Fe and contained more crystalline forms of Fe oxides. Other groups of bacteria found at these two sites include known S- and a few N-metabolizing bacteria, all ubiquitous in marine environments. The low similarity of our clones with the GenBank database suggests that new species and perhaps new families were recovered. The results of this study suggest that Fe-rich hydrothermal sediments, while dominated by Fe oxidizers, can be exploited by a variety of autotrophic and heterotrophic micro-organisms.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".