Effect of crop and residue type on nitrous oxide emissions from rotations in the semi-arid Canadian prairies
Bibliographic record
Abstract
Crop rotations on the Canadian prairies commonly include sequences of pulses, oilseeds, and cereals; however, limited information is available regarding the influence that different crop types and sequences may have on direct nitrous oxide (N2O) emissions. A 3 yr field study was conducted on a site near Scott, SK, to compare N2O emissions from selected crop phases of rotations containing pea (Pisum sativum L.), wheat (Triticum aestivum L.), and canola (Brassica napus L.) and to examine the potential influence of these residues on N2O emissions during the subsequent crop phase. Nitrous oxide losses from N-fertilized canola or wheat crops were generally higher than losses from pea or the control treatments. Nitrous oxide losses from N-fertilized wheat or canola crops grown on pea residue were comparable or lower than losses from N-fertilized wheat or canola crops grown on wheat residues. Cumulative N2O loss over the 3 yr was significantly higher from N-fertilized wheat grown on canola compared with pea or wheat residues. Losses from wheat grown on canola residue were 67% and 56% higher than from wheat grown on pea or wheat residue, respectively. This indicates that the emission factors used to estimate direct N2O loss may need to be adjusted upwards for N-fertilized crops grown on canola compared with wheat or pea residues.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".