Multiscale Regulation of Rhizosphere Microorganisms on the Spatiotemporal Variation of Soil Amino Acid Nitrogen During Plant Succession: A Case Study from the Desert Riparian Ecosystem
Bibliographic record
Abstract
Riparian zones in deserts are critical for water purification, habitat, and soil conservation, but most studies focus on vegetation zones without long-term monitoring, hindering the comprehensive assessment of their long-term benefits. This study on Yuangeda Lake in northwestern China’s desert used spatiotemporal substitution to convert soil spatial heterogeneity into a temporal gradient, analyzing soil properties across vegetation zones formed by lake expansion and microbial impacts on amino acid nitrogen. Results showed that rhizosphere soil amino acid nitrogen was higher in grass ( Agropyron cristatum L. Gaertn.) and grass/shrub mixed ( A. cristatum and Artemisia desertorum Spreng.) areas than in shrub areas ( A. desertorum ). As plant communities persisted, a distinct threshold in rhizosphere soil amino acid nitrogen emerged at approximately 11 years. Rhizosphere bacterial and fungal specific taxonomic units, microbial co-occurrence network topologies, and amino acid nitrogen-related bacterial functions differed between grass/grass-shrub-mixed and shrub areas, linked to plant community duration. Bacteria dominated amino acid nitrogen formation in grass areas, while bacteria and fungi contributed in the grass/shrub mixed area. Overall, water inflow promoted soil amino acid nitrogen accumulation, with microbial contributions varying by plant community duration. This study supports desert riparian soil restoration and management, advancing our understanding of amino acid nitrogen in the nitrogen cycle.
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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 | 0.001 | 0.001 |
| 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".