Patterns and drivers of global gross nitrogen mineralization in soils
Bibliographic record
Abstract
Soil gross nitrogen (N) mineralization (GNM), a key microbial process in the global N cycle, is mainly controlled by climate and soil properties. This study provides for the first time a comprehensive analysis of the role of soil physicochemical properties and climate and their interactions with soil microbial biomass (MB) in controlling GNM globally. Through a meta-analysis of 970 observations from 337 published papers from various ecosystems, we found that GNM was positively correlated with MB, total carbon, total N and precipitation, and negatively correlated with bulk density (BD) and soil pH. Our multivariate analysis and structural equation modeling revealed that GNM is driven by MB and dominantly influenced by BD and precipitation. The higher total N accelerates GNM via increasing MB. The decrease in BD stimulates GNM via increasing total N and MB, whereas higher precipitation stimulates GNM via increasing total N. Moreover, the GNM varies with ecosystem type, being greater in forests and grasslands with high total carbon and MB contents and low BD and pH compared to croplands. The highest GNM was observed in tropical wet soils that receive high precipitation, which increases the supply of soil substrate (total N) to microbes. Our findings suggest that anthropogenic activities that affect soil microbial population size, BD, soil substrate availability, or soil pH may interact with changes in precipitation regime and land use to influence GNM, which may ultimately affect ecosystem productivity and N loss to the environment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 teacher head, 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".