Ammonia volatilization from manure mixed with biochar
Bibliographic record
Abstract
Ammonia (NH 3 ) volatilization from NH 3 -based fertilizer and animal manure reduces their nitrogen (N) fertilizer value and is a source of environmental pollution. Mixing manure with biochar may lower NH 3 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 NH 3 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 NH 3 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 NH 3 volatilization from UAN fertilizer solution when mixed with 25% (v/v) of Dynamotive biochar compared with NH 3 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 NH 3 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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".