Response of soil nitrogen mineralization, nitrification, and denitrification to milk vetch (<i>Astragalus sinicus</i> L.) application in a paddy field
Bibliographic record
Abstract
We conducted incubation experiments with paddy soil collected from a long-term field experiment to explore the effect of Chinese milk vetch ( Astragalus sinicus L., CMV) application on potential nitrogen (N) denitrification (PDA), nitrification (PNA), mineralization (PNM), soil chemical properties, microbial communities, enzyme activities, yields, and nutrient uptake of rice under different fertilization treatments. Five treatments were included: no chemical fertilizers (C0), chemical fertilizers (C100), Chinese milk vetch (M), CMV combined with 100% chemical fertilizers (MC100), and with 80% chemical fertilizers (MC80). Results showed that the M, MC100, and MC80 treatments significantly increased PNM and PNA compared with the C100 treatment ( P < 0.05). Meanwhile, the CMV application significantly increased total N, microbial biomass N, and carbon (C) concentrations, the abundances of the bacterial phylum Actinobacteria and the genera Bradyrhizobium, Mycobacterium, Streptomyces, and Reyranella, N-acetyl-glucosaminidase (NAG) activity, yields, and N nutrient uptake of rice grain compared with the C100 treatment ( P < 0.05). Correlation analyses indicated that grain yield and N uptake of rice, soil total N, microbial biomass C and N, the bacterial phylum Actinobacteria, the genera Bradyrhizobium, Mycobacterium, Streptomyces, Reyranella, and NAG were significantly correlated with PNM under different fertilization regimes, while microbial biomass C and N, Actinobacteria, Bradyrhizobium, and Reyranella were positively related to PNA ( P < 0.05). Together, the application of CMV alone or in combination with chemical fertilizers can improve soil properties and rice growth, which may accelerate N mineralization and nitrification in this soil.
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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) | 0.001 | 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".