Predominance of ammonia-oxidizing archaea and<i>nirK</i>-gene-bearing denitrifiers among ammonia-oxidizing and denitrifying populations in sediments of a large urban eutrophic lake (Lake Donghu)
Bibliographic record
Abstract
The coupled nitrification-denitrification process plays a pivotal role in cycling and removal of nitrogen in aquatic ecosystems. In the present study, the communities of ammonia oxidizers and denitrifiers in the sediments of 2 basins (Guozhenghu Basin and Tuanhu Basin) of a large urban eutrophic lake (Lake Donghu) were determined using the ammonia monooxygenase subunit A (amoA) gene and the nitrite reductase gene. At all sites of this study, the archaeal amoA gene predominated over the bacterial amoA gene, whereas the functional gene for denitrification nirK gene far outnumbered the nirS gene. Spatially, compared with the Tuanhu Basin, the Guozhenghu Basin showed a significantly greater abundance of the archaeal amoA gene but less abundance of the nirK and nirS genes, while there was no significant difference of bacterial amoA gene copy numbers between the 2 basins. Unlike the archaeal amoA gene, the nirK gene showed a significant difference in community structure between the 2 basins. Archaeal amoA diversity was limited to the water-sediment cluster of Crenarchaeota, in sharp contrast with nirK for which 22 distinct operational taxonomic units were found. Accumulation of organic substances were found to be positively related to nirK and nirS gene copy numbers but negatively related to archaeal amoA gene copy numbers, whereas the abundance of the bacterial amoA gene was related to ammonia concentration.
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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 | 0.000 | 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".