Gaseous emissions at different space scales in the nitrogen cycle: A review
Bibliographic record
Abstract
For a long time emissions of gaseous reactive nitrogen has been of less concern in France than nitrate leaching. However, these emissions are known to have significant consequences on the climate, the environment and human health. Estimating emission of reactive nitrogen to the atmosphere at different scales by using classical methods for inventory, as well as databases and models, showed that the largest emission losses are due to N2 from denitrification, followed by ammonia. The total gaseous losses amount to the same magnitude as nitrate. Livestock farming is a large contributor to gaseous nitrogen losses, mainly through ammonia emissions due to the handling of manure that varies from one system of livestock production to another. Processes at the origin of these emissions are described, as well as their drivers linked to soil, the climate and agricultural practices, along with the main means to mitigate such emissions. Measurement methods are also described, with their fields of application and how they have progressively changed over time. The different methods make it possible to cover a wide range of applications, from comparing agronomic treatments (with e.g. static chambers) to estimating emission over large plots or at the landscape scale (micrometeorological methods). This field of research is progressing rapidly, linked mainly with new analytical developments. Emission can also be estimated using a range of models, from emission factor (e.g. IPCC methodology) to ecosystem models, describing nitrogen transfer and transformation in soil in relation with the carbon cycle. These models can be used for national emission inventories as well as for assessing mitigation measures or analysing the interactions between nitrogen and the carbon cycle. Nowadays, accounting for gaseous losses of reactive nitrogen is an agronomic and environmental issue, which must be considered in fertilization management at the field, farm and landscape scales.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".