Genome-resolved metagenomics of nitrogen cycling processes in Saanich Inlet
Bibliographic record
Abstract
In the oxygen-starved water of the Saanich Inlet, genes encoding nitrogen loss processes e.g. denitrification were more abundant than those encoding nitrification or nitrogen fixation pathways. The most abundant denitrification genes recovered along the Saanich Inlet oxygen gradient spanning 100 and 200 meter depth intervals were the nitrate reductase subunits, napA and narG . Minor levels of nitrification genes such as nxrA and nxrB were identified by PROKKA, while the denitrification genes such as napA, narG and nirS were more abundant, implying denitrification pathways were favoured. Similarly, the increased denitrification gene abundances correlates with the accumulation of products typical in the denitrification pathway. For example, nitrate (NO3-), a reactant in the denitrification pathway, is completely consumed at 200 meters, whereas hydrogen sulfide (H2S) concentration sharply increases. Taxonomic grouping of these denitrification genes, identified by Gtdb-Tk, indicate that they are primarily encoded by the Gammaproteobacteria class. Together, these results support a global trend of community metabolism shifting from utilizing oxygen to nitrogen as a terminal electron acceptor in response to oxygen minimum zones, in addition to identifying the key community members involved. This shift towards the denitrification pathways may potentially aggravate climate change in the coastal marine environments through the formation of potent greenhouse gases, mainly nitrous oxide (N2O), in oxygen minimum zones.
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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 | 0.000 | 0.001 |
| 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".