Predicting nitrous oxide emissions after the application of solid manure to grassland in the United Kingdom
Bibliographic record
Abstract
Abstract Nitrous oxide (N2O) 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 N2O flux from the application of solid manure at two grassland sites (North Wyke [NW] and Pwllpeiran [PW]) in the United Kingdom. The simulated N2O 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), R2, 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, R2, 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 N2O emissions for NW (NSE = 0.47, R2 = .63 during calibration, and NSE = 0.55, R2 = .58 during validation) and for PW (NSE = 0.54, R2 = .71 for calibration, and NSE = 0.47, R2 = .69 for validation). Results revealed that the SWAT model performed reasonably well in representing the dynamics of N2O emissions after solid manure application to grassland.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".