Global gross nitrification rates are dominantly driven by soil carbon‐to‐nitrogen stoichiometry and total nitrogen
Bibliographic record
Abstract
Abstract Soil gross nitrification (GN) is a critical process in the global nitrogen (N) cycle that results in the formation of nitrate through microbial oxidation of ammonium or organic N, and can both increase N availability to plants and nitrous oxide emissions. Soil GN is thought to be mainly controlled by soil characteristics and the climate, but a comprehensive analysis taking into account the climate, soil characteristics, including microbial characteristics, and their interactions to better understand the direct and indirect controlling factors of GN rates globally is lacking. Using a global meta‐analysis based on 901 observations from 330 15 N‐labeled studies, we show that GN differs significantly among ecosystem types, with the highest rates found in croplands, in association with higher pH which stimulates nitrifying bacteria activities. Autotrophic and heterotrophic nitrifications contribute 63% and 37%, respectively, to global GN. Soil GN increases significantly with soil total N, microbial biomass, and soil pH, but decreases significantly with soil carbon (C) to N ratio (C:N). Structural equation modeling suggested that GN is mainly controlled by C:N and soil total N. Microbial biomass and pH are also important factors controlling GN and their effects are similar. Precipitation and temperature affect GN by altering C:N and/or soil total N. Soil total N and temperature drive heterotrophic nitrification, whereas C:N and pH drive autotrophic nitrification. Moreover, GN is positively related to nitrous oxide and carbon dioxide emissions. This synthesis suggests that changes in soil C:N, soil total N, microbial population size, and/or soil pH due to anthropogenic activities may influence GN, which will affect nitrate accumulation and gaseous emissions of soils under global climate and land‐use changes.
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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) | 0.000 | 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 | 0.000 | 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 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".