Methane emissions reduced using gypsum in pilot‐scale dairy manure tanks
Bibliographic record
Abstract
Abstract Animal manure storage facilities are sources of greenhouse gas emissions in Canada, with the majority of emissions in the form of methane and nitrous oxide. This study was conducted to assess how effective adding gypsum powder to cow manure is at reducing the emissions of methane and nitrous oxide. At a pilot‐scale research facility in Nova Scotia, Canada, gypsum powder was added to tanks containing ∼10,000 L of local dairy manure in duplicate at three rates: low rate (3.2 g L −1 ), high rate (7.1 g L −1 ), and the control (0 g L −1 ). Each manure tank was enclosed within a steady‐state chamber, and gas samples were manually taken using a syringe from the exhaust into pre‐evacuated vials at regular intervals between June and November (143 days). Using gas chromatography, methane and nitrous oxide measurements were analyzed from each vial, allowing for calculations of cumulative emissions and CO 2 ‐e. Compared to the control, both the high rate and low rate of gypsum significantly reduced cumulative methane emissions. Though the cumulative nitrous oxide emissions were reduced using both rates of gypsum, the nitrous oxide emissions were minor and not statistically significant. Methane emissions were reduced by 87% using the low rate of gypsum and by 92% using the high rates of gypsum. Gypsum additive to slurry in storage facilities is a safe and effective mitigation strategy to reduce greenhouse gas emissions. Further research is required to support on‐farm application rates and feasibility.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".