Ammonia volatilization from manure mixed with biochar
Bibliographic record
Abstract
Ammonia (NH3) volatilization from NH3-based fertilizer and animal manure reduces their nitrogen (N) fertilizer value and is a source of environmental pollution. Mixing manure with biochar may lower NH3 volatilization from manure by adding H+, adsorbing mineral N, or increasing N immobilization in microbial biomass. The objective of this study was to determine whether wood-based biochar could lower NH3 volatilization from vented pails containing manure (liquid swine, dairy slurry, and solid poultry manure) or a urea ammonium nitrate solution (UAN). Two types of wood-based biochar (BlueLeaf and Dynamotive) were mixed with three types of manure and UAN fertilizer solution at 0, 2.5, 5, 10, and 25% biochar by volume in vented pails. After 21 d storage in an outdoor shaded area, the greatest NH3 volatilization was from poultry manure, which had pH 9.4 on average and low water content regardless of the biochar source and application rate. There was less NH3 volatilization from UAN fertilizer solution when mixed with 25% (v/v) of Dynamotive biochar compared with NH3 volatilization from UAN fertilizer solution mixed with 0%–10% (%) of Dynamotive biochar, probably because pH decreased from 7.0 to 6.4 after 21 d contact. Mixing wood-based biochar with manure had no impact on NH3 volatilization, suggesting that these biochar sources did not appreciably change the pH and N dynamics in stored manure after 21 d.
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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) | 0.001 | 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".