Predicting nitrous oxide emissions after the application of solid manure to grassland in the United Kingdom
Bibliographic record
Abstract
Abstract Nitrous oxide (N 2 O) emission from agricultural soils represents a significant source of greenhouse gas to the atmosphere. We evaluated the suitability of a modified Soil and Water Assessment Tool (SWAT) model to estimate the N 2 O flux from the application of solid manure at two grassland sites (North Wyke [NW] and Pwllpeiran [PW]) in the United Kingdom. The simulated N 2 O emissions were validated against field observations measured in 2011 and 2012 for model calibration and validation, respectively. The SWAT model predicts water‐filled pore space (WFPS) very well with Nash–Sutcliffe efficiency (NSE), R 2 , RMSE, and percentage bias (PBIAS) values of 0.67, .72, 0.06, and 3.64, respectively, during the calibration period for NW site, whereas it gives 0.68, .69, 0.07, and 3.04, respectively during the validation period. At PW, the model predicted the NSE, R 2 , RMSE, and PBIAS of 0.55, .69, 0.04, and −4.5, respectively, during calibration and 0.63, .71, 0.05, and −2.6, respectively, during the validation period. Compared with WFPS, the model resulted in a slightly lower fit for N 2 O emissions for NW (NSE = 0.47, R 2 = .63 during calibration, and NSE = 0.55, R 2 = .58 during validation) and for PW (NSE = 0.54, R 2 = .71 for calibration, and NSE = 0.47, R 2 = .69 for validation). Results revealed that the SWAT model performed reasonably well in representing the dynamics of N 2 O emissions after solid manure application to grassland.
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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 | 0.001 | 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 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".