Abundance and composition dynamics of soil ammonia-oxidizing archaea in an alpine fir forest on the eastern Tibetan Plateau of China
Bibliographic record
Abstract
Real-time qPCR and clone library sequencing targeting amoA genes were used to investigate the seasonal dynamics of an ammonia-oxidizing archaea (AOA) community in an alpine fir forest in western China. AOA were detected at all sampling dates, and there were significant variations in archaeal amoA gene copy numbers (7.63 × 10(5) to 8.35 × 10(8) per gram of dry soil) throughout the nongrowing season. Compared with ammonia-oxidizing bacteria (AOB), the AOA displayed a higher abundance on the majority of sampling dates during the freeze-thaw period. All of the AOA sequences fell within soil and sediment lineages and were affiliated with 7 clusters. Compared with the other clusters, cluster 1 was more sensitive to low temperature and was the dominant group in August. In contrast, cluster 3 dominated the AOA community in winter and probably represents a group of cold-adapted archaea. Redundancy analysis (RDA) revealed that the seasonality of the AOA community was mainly attributed to changes in soil temperature and nutrient availability (e.g., dissolved organic nitrogen and carbon). Our results indicate that AOA exist in frozen soils in the alpine coniferous forest ecosystem of the eastern Tibetan Plateau. Moreover, soil temperature may directly and (or) indirectly affect AOA abundance and composition and may further influence the soil N cycle during the winter.
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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".