Distribution of typical denitrifying functional genes and diversity of the <i>nirS</i> -encoding bacterial community related to environmental characteristics of river sediments
Bibliographic record
Abstract
Abstract. Denitrification in river sediments leads to nitrate removal from the aquatic system; therefore, it is necessary to understand functional diversity of denitrifier communities in the system. Sediment samples (0–25 cm depth) were collected from three typical locations along the Pearl River. The real-time PCR approach was used to measure the abundance of nitrate (narG), nitrite (nirS, nirK and nrfA), and nitrous oxide (nosZ) reductase genes from the sediment samples. Assemblages of nirS, nirK and nosZ indicated that complete denitrification occurred in sediment cores, with the greatest number of gene copies from 5–15 cm depth. Dissimilatory nitrate reduction appeared to be important below 15 cm depth, based on increasing gene copies of narG and nrfA with sediment depth. There was a close match (78–94 %) between the nirS sequences recovered from Pearl River sediment and those detected in estuarine and marine sediments as well as active sludge, suggesting that domestic sewage inputs and irregular tides. Canonical correspondence analysis indicated that the spatial distribution of denitrifying bacteria was highly correlated with dissolved inorganic N (DIN: NH4+, NO2
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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) | 0.001 | 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".