Estimating global terrestrial denitrification from measured N2O:(N2O + N2) product ratios
Bibliographic record
Abstract
The use of nitrogen (N) fertilizers and cultivation of N-fixing crops has grown exponentially over the last century, with severe environmental consequences. Most of the anthropogenic reactive nitrogen will ultimately be returned by denitrification to the atmosphere as inert N2, but the magnitude of denitrification and the ratio of N2O to (N2O + N2) emitted (RN2O) is unknown for the vast majority of terrestrial ecosystems. This paper provides estimates of terrestrial denitrification and RN2O by reviewing existing literature and compiling a N budget for the global land surface. We estimate that terrestrial denitrification has doubled from 80 Tg-N year−1 in pre-industrial times to 160 Tg-N year−1 in 2005 with a mean RN2O of approximately 0.08. We conclude that upscaling of RN2O can provide spatial estimates of terrestrial denitrification when data from acetylene inhibition methods are excluded. Recent advances in methodologies to measure N2 emissions and RN2O under field conditions could open the way for more effective management of terrestrial N flows.
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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 | 0.000 |
| 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".