Diversity, Abundance, and Potential Activity of Nitrifying and Nitrate-Reducing Microbial Assemblages in a Subglacial Ecosystem
Bibliographic record
Abstract
Subglacial sediments sampled from beneath Robertson Glacier (RG), Alberta, Canada, were shown to harbor diverse assemblages of potential nitrifiers, nitrate reducers, and diazotrophs, as assessed by amoA, narG, and nifH gene biomarker diversity. Although archaeal amoA genes were detected, they were less abundant and less diverse than bacterial amoA, suggesting that bacteria are the predominant nitrifiers in RG sediments. Maximum nitrification and nitrate reduction rates in microcosms incubated at 4°C were 280 and 18.5 nmol of N per g of dry weight sediment per day, respectively, indicating the potential for these processes to occur in situ. Geochemical analyses of subglacial sediment pore waters and bulk subglacial meltwaters revealed low concentrations of inorganic and organic nitrogen compounds. These data, when coupled with a C/N atomic ratio of dissolved organic matter in subglacial pore waters of ~210, indicate that the sediment communities are N limited. This may reflect the combined biological activities of organic N mineralization, nitrification, and nitrate reduction. Despite evidence of N limitation and the detection of nifH, we were unable to detect biological nitrogen fixation activity in subglacial sediments. Collectively, the results presented here suggest a role for nitrification and nitrate reduction in sustaining microbial life in subglacial environments. Considering that ice currently covers 11% of the terrestrial landmass and has covered significantly greater portions of Earth at times in the past, the demonstration of nitrification and nitrate reduction in subglacial environments furthers our understanding of the potential for these environments to contribute to global biogeochemical cycles on glacial-interglacial timescales.
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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 | 0.001 |
| 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".