Responses of the rhizosphere microbiome to long-term nitrogen addition in a boreal forest
Bibliographic record
Abstract
As the interface between plants and soil, the response of the rhizosphere to nitrogen deposition is particularly important. The rhizosphere responses to nitrogen deposition are not consistent. Here, four levels of nitrogen addition treatments were applied in boreal forests of China for 8 years: control, low nitrogen, medium nitrogen, and high nitrogen. We analyzed the soil properties and microbial communities of rhizosphere and bulk soils under different nitrogen application levels and assessed the relationship between the main species and environmental factors. The results showed that long-term nitrogen addition significantly reduced the content of total carbon in the soil and increased the soil microbial biomass nitrogen in the rhizosphere. The total carbon content in the rhizosphere under high nitrogen was 40.79 g·kg−1, which was significantly lower than that in the control (51.82 g·kg−1). The diversity of the rhizosphere and bulk soil fungal communities had different significant responses to nitrogen addition. Some specific microbial taxa could be used as rhizosphere bacterial biomarkers under different nitrogen treatments. The soil microbial biomass nitrogen was the significant environmental variable affecting bacterial species variation in the rhizosphere. Nitrogen addition will change the diversity of the rhizosphere microbial community by changing soil microenvironmental factors.
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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 | 0.001 | 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".