High plant diversity alleviates the negative effects of nitrogen deposition on soil nitrogen cycling multifunctionality
Bibliographic record
Abstract
Introduction: Changes in plant diversity and increased atmospheric nitrogen deposition independently influence soil nitrogen cycling in terrestrial ecosystems. However, the interactive effects of plant diversity and nitrogen deposition on soil nitrogen cycling multifunctionality (NCMF) in grassland ecosystems remain poorly understood. Methods: ) and plant diversity gradients (1, 3, and 6 species). Results: Our results revealed a significant interactive effect between plant diversity and nitrogen addition on soil NCMF. Specifically, high plant diversity alleviated the negative effects of nitrogen addition on soil NCMF. The addition of nitrogen reduced the soil pH, which imposed microbial stress by limiting carbon availability. In contrast, higher plant diversity increased soil organic matter via below-ground carbon inputs, thereby reducing the soil carbon limitation of microorganims and enhancing the soil NCMF. Discussion: Overall, our findings suggest that maintaining or enhancing plant diversity in grasslands could be a key strategy to mitigate the adverse effects of atmospheric nitrogen deposition on soil nitrogen cycling, highlighting the crucial role of plant diversity in regulating ecosystem nutrient cycling under global change.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".