Abundance and community composition of ammonia-oxidizing archaea and bacteria in two different zones of Lake Taihu
Bibliographic record
Abstract
The abundance and community composition of ammonia-oxidizing archaea and ammonia-oxidizing bacteria in the surface sediments of 2 different zones (Meiliang Bay and Eastern Lake Taihu) of Lake Taihu were investigated using real-time quantitative polymerase chain reaction and clone libraries. The amoA gene copy numbers in the surface sediment of Meiliang Bay ranged from 4.91 × 10(5) to 8.65 × 10(6) copies/g dry sediment for the archaeal amoA gene and from 3.74 × 10(4) to 3.86 × 10(5) copies/g dry sediment for the bacterial amoA gene, which were significantly higher than those of Eastern Lake Taihu (P < 0.05). Concentrations of ammonia (NH(4)(+)), total nitrogen, organic matter, and pH of the sediments exhibited significantly negative correlations with the abundance of ammonia-oxidizing archaea or ammonia-oxidizing bacteria (P < 0.05 or P < 0.01, respectively). The potential nitrification rates show remarkable correlations with the copy numbers of the archaeal amoA gene. Diversity of the archaeal amoA gene in Eastern Lake Taihu was significantly higher than that of Meiliang Bay, whereas the bacterial amoA gene diversity was comparable for the 2 lake zones. The data obtained in this study would be useful to elucidate the role of ammonia-oxidizing archaea and ammonia-oxidizing bacteria in the nitrogen cycle of freshwater ecosystems.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".