Long‐term effects of nitrogen and phosphorus fertilization on soil microbial community structure and function under continuous wheat production
Bibliographic record
Abstract
Soil microorganisms play a critical role in the biosphere, and the influence of cropland fertilization on the evolution of soil as a living entity is being actively documented. In this study, we used a shotgun metagenomics approach to globally expose the effects of 50-year N and P fertilization of wheat on soil microbial community structure and function, and their potential involvement in overall N cycling. Nitrogen (N) fertilization increased alpha diversity in archaea and fungi while reducing it in bacteria. Beta diversity of archaea, bacteria and fungi, as well as soil function, were also mainly driven by N fertilization. The abundance of archaea was negatively impacted by N fertilization while bacterial and fungal abundance was increased. The responses of N metabolism-related genes to fertilization differed in archaea, bacteria and fungi. All archaeal N metabolic processes were decreased by N fertilization, while denitrification, assimilatory nitrate reduction and organic-N metabolism were highly increased by N fertilization in bacteria. Nitrate assimilation was the main contribution of fungi to N cycling. Thaumarchaeota and Halobacteria in archaea; Actinobacteria, Alphaproteobacteria, Betaproteobacteria, Gammaproteobacteria and Deltaproteobacteria in bacteria; and Sordariomycetes in fungi participated dominantly and widely in soil N metabolic processes.
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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".