Response Curves for Ammonia and Methane Emissions From Stored Liquid Manure Receiving Low Rates of Sulfuric Acid
Bibliographic record
Abstract
Addition of sulfuric acid (H 2 SO 4 ) to liquid dairy manure (slurry) reduces methane (CH 4 ), nitrous oxide (N 2 O), and ammonia (NH 3 ) emissions. There is interest in understanding how gaseous emissions respond to decreasing rates of acidification, to determine economically optimum application rates. Acidification rates were tested ranging from 0 to 2 g sulfuric acid (H 2 SO 4 ) L −1 slurry in six meso-scale outdoor storage tanks, each filled with 10.6 m 3 slurry and stored for 114 d. Results showed that the rate of acidification for maximum inhibition of CH 4 and NH 3 emissions varied markedly, whereas N 2 O reductions were modest. Reductions of CH 4 increased with acid rate from 0 to 1.2 g L −1 , with no additional response beyond >1.2 g L −1 . In contrast to CH 4 , inhibitions of NH 3 showed a linear response across all rates, although reductions were ≤ 30%. Thus, higher acidification rates would be required to achieve greater NH 3 emission reductions. Our findings indicate that achieving >85% NH 3 emissions reductions would require 4 × more acid than achieving >85% CH 4 reductions. Decisions on optimum H 2 SO 4 rates will depend on the need to mitigate CH 4 emissions (the primary greenhouse gas emitted from stored liquid manure) or reduce NH 3 emissions (which is regulated in some regions). These results will help develop guidelines related to the potential costs and benefits of reducing emissions through acidification.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 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".