The stoichiometry of soil microbial biomass determines metabolic quotient of nitrogen mineralization
Bibliographic record
Abstract
Abstract Soil nitrogen (N) mineralization is crucial for the sustainability of available soil N and hence ecosystem productivity and functioning. Metabolic quotient of N mineralization (Q min), which is defined as net soil N mineralization per unit of soil microbial biomass N, reflects the efficiency of soil N mineralization. However, it is far from clear how soil Q min changes and what are the controlling factors at the global scale. We compiled 871 observations of soil Q min from 79 published articles across terrestrial ecosystems (croplands, forests, grasslands, and wetlands) to elucidate the global variation of soil Q min and its predictors. Soil Q min decreased from the equator to two poles, which was significant in the North Hemisphere. Soil Q min correlated negatively with soil pH, total soil N, the ratio of soil carbon (C) to N, and soil microbial biomass C, and positively with mean annual temperature and C:N ratio of soil microbial biomass at a global scale. Soil microbial biomass, climate, and soil physical and chemical properties in combination accounted for 41% of the total variations of global soil Q min. Among those predictors, C:N ratio of soil microbial biomass was the most important factor contributing to the variations of soil Q min (the standardized coefficient = 0.39) within or across ecosystem types. This study emphasizes the critical role of microbial stoichiometry in soil N cycling, and suggests the necessity of incorporating soil Q min into Earth system models to better predict N cycling under environmental 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".