Agricultural soil denitrifiers possess extensive nitrite reductase gene diversity
Bibliographic record
Abstract
Summary Denitrification transforms nitrogen applied as fertilizer and emits N 2 O, which is a potent greenhouse gas. Very little is known about the identities of abundant and active denitrifiers in agricultural soils. We coupled DNA stable‐isotope probing (DNA‐SIP) with flow‐through reactors (FTRs) to detect active agricultural soil denitrifiers. The FTRs were incubated with nitrate and 13 C 6 ‐glucose under anoxic conditions and sampled at multiple time points. Labelled DNA from active microorganisms was analyzed by 16S rRNA gene fingerprinting, amplicon and shotgun metagenomic sequencing. Taxonomic representation of heavy fractions was consistent across sites and time points, including Betaproteobacteria (71%; Janthinobacterium , Acidovorax , Azoarcus and Dechloromonas ), Alphaproteobacteria (8%; Rhizobium ), Gammaproteobacteria (4%; Pseudomonas ) and Actinobacteria (4%; Streptomycetaceae ). Most nitrite‐reductase reads from heavy DNA annotated to the copper‐containing form ( nirK ). Assigned taxonomies of active denitrifiers based on reads matching the nirK gene were comparable to those obtained through nitric oxide ( norB ) and RNA polymerase ( rpoB ) annotations but not the nitrous oxide reductase gene ( nosZ ). Analysis of recovered metagenomes from heavy DNA demonstrated extensive nirK sequence family diversity, including novel taxonomic groups that are not captured by existing primers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.001 | 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".