Long-term nitrogen fertilization shaped the <i>nifH</i>, <i>nirK</i>, and <i>nosZ</i> gene community patterns in red paddy soil in south China
Bibliographic record
Abstract
To understand the diversities of diazotrophs and denitrifiers in red paddy soil under long-term fertilization conditions, nifH, nirK, and nosZ libraries were constructed by PCR–RFLP. nirK gene diversity proved to be lower than that of nosZ and nifH, and nirK and nosZ genes were more sensitive to different fertilization treatments than the nifH gene was. The 3 libraries were dominated by diverse microbes, including the Alpha, Beta, Gamma, and Delta subclasses of the Proteobacteria. Long-term addition of urea with straw mulch and azophoska increased the abundance of nonsymbiotic diazotrophs, which indicated that nonsymbiotic diazotrophs were responsible for the majority of the nitrogen-fixing ability in paddy soil. In addition, a potential link between nifH and nosZ was found due to the existence of nitrogen fixers, such as Bradyrhizobium and Ralstonia, in the nosZ library. The main chemical factors affecting the 3 genes were identified: pH was the most important factor of the nifH community; the nirK gene was more affected by pH and organic matter; available potassium and the carbon-to-nitrogen ratio significantly influenced the community structure of the nosZ gene.
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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".