Global variations and controlling factors of anammox rates
Bibliographic record
Abstract
Abstract Soil anammox is an environmentally friendly way to eliminate reactive nitrogen (N) without generating nitrous oxide. Nevertheless, the current earth system models have not incorporated the anammox due to the lack of parameters in anammox rates on a global scale, limiting the accurate projection for N cycling. A global synthesis with 1212 observations from 89 peer‐reviewed papers showed that the average anammox rate was 1.60 ± 0.17 nmol N g−1 h−1 in terrestrial ecosystems, with significant variations across different ecosystems. Wetlands exhibited the highest rate (2.17 ± 0.31 nmol N g−1 h−1), followed by croplands at 1.02 ± 0.09 nmol N g−1 h−1. The lowest anammox rates were observed in forests and grasslands. The anammox rates were positively correlated with the mean annual temperature, mean annual precipitation, soil moisture, organic carbon (C), total N, as well as nitrite and ammonium concentrations, but negatively with the soil C:N ratio. Structural equation models revealed that the geographical variations in anammox rates were primarily influenced by the N contents (such as nitrite and ammonium) and abundance of anammox bacteria, which collectively accounted for 42% of the observed variance. Furthermore, the abundance of anammox bacteria was well simulated by the mean annual precipitation, soil moisture, and ammonium concentrations, and 51% variance of the anammox bacteria was accounted for. The key controlling factors for soil anammox rates differed from ecosystem type, for example, organic C, total N, and ammonium contents in croplands, versus soil C:N ratio and nitrite concentrations in wetlands. The controlling factors in soil anammox rate identified by this study are useful to construct an accurate anammox module for N cycling in earth system models.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".