Abundance, geo-microbial role and community diversity of sulfate-reducing bacteria in tropical estuarine sediments
Bibliographic record
Abstract
Sulfate-reducing bacteria (SRB), a diverse taxonomic group of anaerobic microorganisms that oxidize sulfur compounds as terminal electron acceptors, drive the key biogeochemical process of sulfate reduction. However, little is known about the diversity of SRB in tropical coastal habitats. Thus, the goal of this study is to explore and compare the community diversity of the Ashtamudi and Vembanad estuaries and evaluate the sedimentary sulfate reduction rate. The microbiome profiling of SRB was carried out using Next Generation Sequencing (NGS) techniques (Illumina sequencing) based on gene amplification of the dissimilatory sulfate reductase (dsr) enzyme to confirm the diversity and dominant taxa of SRB. The diversity of SRB represented by the Shannon alpha diversity index of the Ashtamudi estuary (6.55) was two-fold that of the Vembanad estuary. In the Vembanad estuary, the marine zone (4.09) harboured more diversity than the freshwater zone (1.825). Higher diversity and lower abundant taxa in the Ashtamudi estuary pointed out the presence of a rare biosphere that has not yet been studied. It was interesting to note that both estuaries have the same SRB phylum diversity. At the level of species, however, there was a noticeable difference. This differential diversity of SRB has an apparent influence on methane production during the breakdown of organic matter in addition to sulfate reduction. Several species of SRB have an important role in the biodegradation of persistent pollutants and the removal of heavy metals.
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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 | 0.000 | 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) | 0.001 | 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".