Le protoxyde d’azote (N<sub>2</sub>O), puissant gaz à effet de serre émis par les sols agricoles : méthodes d’inventaire et leviers de réduction
Bibliographic record
Abstract
Nitrous oxide (N2O) is a greenhouse gas that mainly originates from soils and agricultural activities. International initiatives require that countries calculate national inventories of their N2O emissions from agricultural soils. Several methodologies can be applied: (i) Tier I Intergovernmental Panel on Climate Change (IPCC) default approach that only takes into account nitrogen (N) input, (ii) Country-specific methodologies (Tier II and Tier III) that account for regional climatic and land use impacts on N2O emission factors, and include several sources. Strategies to mitigate N2O emissions from agricultural soils are based on a rational use of N resource and the stimulation of soil aerobic conditions and biological activity. Management practices to reduce the N2O emissions should be focused on: (i) Avoiding the soil denitrification process by maximizing soil aeration and reducing their acidity, (ii) Improving N fertilization by reducing free N in soil and optimizing N use efficiency in cropping systems, (iii) Direct actions on the microbial processes by limiting the nitrification process and stimulating the last step of the denitrification process (N2O reduction to N2).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".