Isotopic composition of tropospheric and soil N<sub>2</sub>O from successive depths of agricultural plots with contrasting crops and nitrogen amendments
Bibliographic record
Abstract
Agricultural soils are the dominant contributor to increases in atmospheric nitrous oxide (N2O). Few studies have investigated the natural N and O isotopic composition of soil N2O. We collected soil gas samples using horizontal sampling tubes installed at successive depths under five contrasting agricultural crops (e.g., unamended alfalfa, fertilized cereal), and tropospheric air samples. Mean δ15N and δ18O values of soil N2O ranged from −28.0 to +8.9‰, and from +29.0 to +53.6‰. The mean δ15N and δ18O values of tropospheric N2O were +4.6 ± 0.7‰ and +48.3 ± 0.2‰, respectively. In general, δ values were lowest at depth, they were negatively correlated to soil [N2O], and δ15N was positively correlated to δ18O for every treatment on all sampling dates. N2O from the different agricultural treatments had distinct δ15N and δ18O values that varied among sampling dates. Fertilized treatments had soil N2O with low δ values, but the unamended alfalfa yielded N2O with the lowest δ values. Diffusion was not the predominant process controlling N2O concentration profiles. Based on isotopic and concentration data, it appears that soil N2O was consumed, as it moved from deeper to shallower soil layers. To better assess the main process(es) controlling N2O within a soil profile, we propose a conceptual model that integrates data on net N2O production or consumption and isotopic data. The direct local impact of agricultural N2O on the isotopic composition of tropospheric N2O was recorded by a shift toward lower δ values of locally measured tropospheric N2O on a day with very high soil N2O emissions.
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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 | 0.000 | 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 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".