Nitrous Oxide Emissions from Clayey Soils Amended with Paper Sludges and Biosolids of Separated Pig Slurry
Bibliographic record
Abstract
Wastes from animal production and from the paper industry are often used as amendments to agricultural soils. Although data exist on the impacts of raw animal wastes on NO production, little is known regarding the effects of paper wastes and biosolids from treated animal waste. We measured NO emissions for two consecutive snow-free seasons (mid-May through the end of October) from poorly drained clayey soils under corn ( L.). Soils were amended with raw pig slurry (PS) or biosolids (four PS-derived and two paper sludges) and compared with soils with mineral N fertilizer (CaNHNO) or without N addition (Control). Area-based NO emissions from the mineral N fertilizer (average, 8.2 kg NO-N ha; 4.2% of applied N) were higher ( < 0.001) than emissions from the organic amendments, which ranged from 1.5 to 6.1 kg NO-N ha (-0.4 to 2.5% of applied N). The NO emissions were positively correlated with mean soil NO availability (calculated as "NO exposure"), which was highest with mineral N fertilizer. In plots treated with organic amendments (i.e., biosolids and raw PS), NO exposure was negatively correlated to the C:N ratio of the amendment. This resulted in lower NO emissions from the higher C:N ratio biosolids, especially compared with the low C:N ratio PS. Application of paper sludge or PS-derived biosolids to these fine-textured soils, therefore, reduced NO emissions compared with raw PS and/or mineral N fertilizers ( < 0.01).
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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 | 0.001 | 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".