Soil profile N2O efflux from a cotton field in arid Northwestern China in response to irrigation and nitrogen management
Bibliographic record
Abstract
It remains uncertain how different N inputs as synthetic fertilizer or manure and irrigation types affect nitrous oxide (N2O) production and effluxes in the subsurface. A field trial was carried out in 2016 to evaluate the impacts of conventional urea, animal manure, and a 50/50 mix of urea and manure on N2O production/effluxes from a cotton (Gossypium hirsutum L.) field under flood or drip irrigation in northwestern China. Soil N2O concentrations were monitored at 5, 15, 30, and 60 cm depths to assess the production and diffusion rates of N2O in the soil profile. The results showed that N2O concentrations in 0–60 cm ranged between 221 and 532 nL L−1 and averaged 344 nL L−1, which was generally lower compared to other studies in the same region. Manure and flood irrigation significantly increased N2O production at 0–5 cm and 5–15 cm, respectively. That is, the effects of nitrogen management and irrigation types on the N2O production of the profile were reflected in the surface layers and subsurface layers, respectively. All N2O production occurred in the 0–15 cm layer, with the 0–5 cm depth contributing 87%–100% of the surface emissions. The response discrepancy of N2O production/diffusion to irrigation and nitrogen management in different soil depths should be fully considered in developing agricultural N2O emission reduction measures.
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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".