Diversity and abundance of Bacteria and nirS-encoding denitrifiers associated with the Juan de Fuca Ridge hydrothermal system
Bibliographic record
Abstract
Denitrification, which results in the loss of bioavailable nitrogen—an essential macronutrient for all living organisms—may potentially affect chemosynthetic primary production in hydrothermal vent ecosystems where sub-oxic conditions favorable to denitrification are common. Here we describe the diversity and abundance of denitrifying bacteria in the subsurface biosphere at Axial Volcano and the Endeavour Segment on the Juan de Fuca Ridge using a combination of quantitative polymerase chain reaction assays, and small subunit ribosomal RNA (SSU or 16S rRNA) pyrotag and nitrite reductase (nirS) clone library sequencing methods. Bacterial communities were diverse and dominated by members of the ε- and γ-proteobacteria, including taxonomic groups containing known denitrifiers. Assemblages of denitrifiers that could be evaluated by nirS gene sequence comparisons showed low diversity. The single nirS sequence shared by the two locations, affiliated with a γ-proteobacteria isolated from estuarine sediments (Pseudomonas sp. BA2), represented more than half of all sequences recovered when clustered at 97 % identity. All other nirS sequences clustered into different taxonomic groups, indicating important differences in denitrifier community membership between the two sites. Total nirS gene abundance was at least two orders of magnitude lower than 16S rRNA abundance. Overall, our results demonstrate that the diversity and abundance of the nirS gene-containing bacterial community are rather low, as might be expected under the extreme conditions encountered in the subsurface biosphere of hydrothermal vent systems, and do not correlate clearly with any environmental variables investigated (i.e., pH, temperature, and H2S, NO3 −, NH4 + concentrations).
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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 | 0.000 | 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".