Abundance and diversity of ammonia-oxidizing archaea and bacteria in the rhizosphere soil of three plants in the Ebinur Lake wetland
Bibliographic record
Abstract
Ammonia oxidation is carried out by ammonia-oxidizing bacteria (AOB) and ammonia-oxidizing archaea (AOA). The Ebinur Lake wetland is the best example of a temperate arid zone wetland ecosystem in China. Soil samples were collected from rhizosphere and non-rhizosphere soil containing Halocnemum strobilaceum (samples H and H′), Phragmites australis (samples R and R′), and Karelinia caspia (samples K and K′) to study the relationship between environmental factors and the community structure of AOB and AOA. Phylogenetic analysis showed that the AOA sequences belonged to the Nitrosopumilus and Nitrososphaera clusters. AOB were grouped into Nitrosospira sp. and Nitrosomonas sp. Quantitative polymerase chain reaction results showed that the AOA abundance ranged from 2.09 × 104 to 2.94 × 105 gene copies/g soil. The highest number of AOA was detected in sample K, followed by samples R and H. AOB abundance varied between 2.91 × 105 and 1.05 × 106 gene copies/g soil, which was higher than that of AOA. Redundancy analysis indicated that electrical conductivity, pH, and NH4+-N might influence the community structure of AOA and AOB. AOB might play a more crucial role than AOA in ammonia oxidation based on AOB’s higher diversity and abundance in the Ebinur Lake wetland in Xinjiang.
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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 | 0.000 |
| Science and technology studies | 0.001 | 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".