Impact of Topography, Annual Burning, and Nitrogen Addition on Soil Microbial Communities in a Semiarid Grassland
Bibliographic record
Abstract
To gain insight into microbial responses to topography, annual burning, and N addition, a field experiment was conducted from April 2005 to December 2009 in a semiarid grassland of northern China. Soil physicochemical properties, microbial biomass, and microbial community composition were measured in 2006 and 2008. A larger ratio of fungi/bacteria was observed in the upper slope than in the lower slope. Interannual climate fluctuation could have modified the effects of topography on microbial biomass and composition. Burning effects on microbial biomass and composition also depended on year, which could be attributed to low fire severity resulting from decreasing fuel load over time or microbial resilience. Nitrogen addition exerted a much stronger influence on microbial biomass in 2008 compared with 2006 and reshaped microbial communities through decreasing the relative proportion of fungal groups [arbuscular mycorrhizal fungi (AMF) and nonmycorrhizal fungi] in 2008. Overall, these results highlight dynamic responses of soil microbial communities to both the intrinsic features (topography) and exogenous disturbances (fire or N deposition) of the semiarid grassland.
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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".