Functional differentiation of nitrate-reducing isolates regulated by long-term fertilization in a rice paddy soil
Bibliographic record
Abstract
Fertilization can cause obvious shifts in nitrate-reducing community composition in agricultural ecosystems; however, little is known about the behaviors and functional characters of isolated nitrate reducers adapted to a specific environment. In this study, 849 nitrate-reducing bacteria were isolated from various fertilization treatments in a long-term paddy field experiment; the isolates were further characterized in functions with both culture-dependent and independent methodologies. The results showed that CK (no fertilizer) treatment had four genera with even relative abundance, whereas the other three treatments had their own predominant genera with Chromobacterium in nitrogen (N) fertilizer, Serratia in NPK (nitrogen, phosphorus, and potassium fertilizer) and Enterobacter in NPKC treatment (NPK plus rice straw). The isolates of Serratia and Enterobacter grew faster and produced significantly more nitrites than those of Chromobacterium and Burkholderia in the normal growth medium, suggesting that the dominant isolates from nutrient-rich environment, such as NPK and NPKC treatments, are better adapted to high nutritional conditions. On the contrary, the strains of Chromobacterium and Burkholderia possessed stronger nitrite production ability in comparison with the isolates of Serratia and Enterobacter in the diluted growth medium, indicating that the selected isolates from CK and N treatments have the capability to develop under nutrient-limiting conditions. Our results indicated that the behaviors and functions of nitrate reducers appears to be important in adapting to their dwelling habitats.
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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 | 0.000 | 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".