Microbes drive global soil nitrogen mineralization and availability
Bibliographic record
Abstract
Abstract Soil net nitrogen mineralization rate (N min ), which is critical for soil nitrogen availability and plant growth, is thought to be primarily controlled by climate and soil physical and/or chemical properties. However, the role of microbes on regulating soil N min has not been evaluated on the global scale. By compiling 1565 observational data points of potential net N min from 198 published studies across terrestrial ecosystems, we found that N min significantly increased with soil microbial biomass, total nitrogen, and mean annual precipitation, but decreased with soil pH. The variation of N min was ascribed predominantly to soil microbial biomass on global and biome scales. Mean annual precipitation, soil pH, and total soil nitrogen significantly influenced N min through soil microbes. The structural equation models ( SEM ) showed that soil substrates were the main factors controlling N min when microbial biomass was excluded. Microbe became the primary driver when it was included in SEM analysis. SEM with soil microbial biomass improved the N min prediction by 19% in comparison with that devoid of soil microbial biomass. The changes in N min contributed the most to global soil NH 4 + ‐N variations in contrast to climate and soil properties. This study reveals the complex interactions of climate, soil properties, and microbes on N min and highlights the importance of soil microbial biomass in determining N min and nitrogen availability across the globe. The findings necessitate accurate representation of microbes in Earth system models to better predict nitrogen cycle 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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".