Seasonal variation of N<sub>2</sub>O emissions in France inferred from atmospheric N<sub>2</sub>O and<sup>222</sup>Rn measurements
Bibliographic record
Abstract
Nitrous oxide (N2O) concentrations and222Rn activities are measured semi‐continuously at three stations in France: Gif‐sur‐Yvette (a semi‐urban station near Paris), Trainou tower (a rural station) and Puy‐de‐Dôme (a mountain site). From 2002 to 2011, we have found a mean rate of N2O increase of 0.7 pbb a−1. The analysis of the mean diurnal N2O and222Rn cycles shows maximum variabilities at the semi‐urban site of Gif‐sur‐Yvette (0.96 ppb for N2O and 2 Bq m−3for222Rn) compared to the rural site of Trainou tower (0.32 ppb for N2O and 1.3 Bq m−3for222Rn). The use of222Rn as a tracer for vertical mixing and atmospheric transport, combined with the semi‐continuous N2O measurements, allows estimation of N2O emissions by applying the Radon‐Tracer‐Method. Mean N2O emissions values between 0.34 ± 0.12 and 0.51 ± 0.18 g(N2O) m−2a−1and 0.52 ± 0.18 g(N2O) m−2a−1were estimated in the catchment area of Gif‐sur‐Yvette and Trainou, respectively. The mean annual N2O fluxes at Gif‐sur‐Yvette station correlate well with annual precipitation. A 25% increase in precipitation corresponds to a 32% increase in N2O flux. The N2O fluxes calculated with the Radon‐Tracer‐Method show a seasonal cycle, which indicates a strong contribution from the agricultural source, with the application of fertilizers in the early spring inducing a strong increase in N2O emissions. Finally, the results of the Radon‐Tracer‐Method agree well with the national and global emission inventories, accounting for the uncertainties of both methods.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".