Abundance and composition of denitrifiers in response to<i>Spartina alterniflora</i>invasion in estuarine sediment
Bibliographic record
Abstract
Nitrite reduction is regulated by nitrite reductase encoded by nirK and nirS genes. This study aimed to investigate the abundance and composition of nirK- and nirS-containing denitrifiers in response to Spartina alterniflora invasion at the Jiulong River estuary, China. The sediment samples (depth: 0-5.0 and 5.1-20 cm) were collected from 3 vegetation zones, 1 dominated by the exotic plant S. alterniflora, 1 dominated by the native plant Kandelia candel, and 1 dominated by the native plant Cyperus malaccensis, and from an unvegetated flat zone. nirK- and nirS-containing denitrifier population sizes were lower in the invaded and nonvegetated zones than in those dominated by native K. candel and C. malaccensis, which were impacted by depth - vegetation species interaction. The ratios of nirS to nirK abundance ranged from 42.10 to 677.27, with the lowest ratio found for the upper layer in the invaded zone. The nirK-containing denitrifier compositions showed a 35% similarity between invaded zone and others. Most of the sequences of nirK genes recovered from the S. alterniflora zone were specific and distinct from those of nirK genes recovered from other vegetation types; nirS genes in the invaded zone were highly divergent. These results reveal that S. alterniflora invasion has a significant effect on the abundance and composition of both nirK- and nirS-containing denitrifiers, and nirS-containing denitrifiers were less responsive to invasion than nirK-containing denitrifiers.
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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 | 0.000 |
| 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".