Distinct distribution patterns of proteobacterial <i>nirK</i>- and <i>nirS</i>-type denitrifiers in the Yellow River estuary, China
Bibliographic record
Abstract
Denitrification is considered to be the critical process in removing reactive nitrogen in estuarine ecosystems. In the present study, the abundance, diversity, and community structure of nirK- and nirS-type denitrifiers were compared in sediments from the Yellow River estuary. Quantitative polymerase chain reaction showed that the 2 types of denitrifiers exhibited different distribution patterns among the samples, indicating their distinct habitat preference. Phylogenetic analysis revealed that most of the sequences from clusters I, III, IV, and V for nirK-type denitrifiers were dominant and were distributed at sites where dissolved oxygen (DO) was lower, and the sequences in the other clusters were dominant at sites with higher DO. However, there was no spatially heterogeneous distribution for the nirS-type denitrifier community. Canonical correlation analysis and correlation analysis demonstrated that the community structure of nirK was more responsive to environmental factors than was that of nirS. Inversely, the abundance and α-diversity targeting nirS gene could be more easily influenced by environmental parameters. These findings can extend our current knowledge about the distribution patterns of denitrifying bacteria and provide a basic theoretical reference for the dynamics of denitrifying communities in estuarine ecosystem of China.
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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 | 0.000 | 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".