Sulfate Additives Cut Methane Emissions More Effectively at Lower Liquid Manure Storage Temperatures
Why this work is in the frame
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Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Reducing methane (CH 4 ) emissions from dairy farms is a key objective in limiting total greenhouse gas emissions from the livestock industry. Reducing CH 4 emissions from manure storage using additives may provide an achievable near-term contribution to this long-term goal in alignment with the International Dairy Federation’s initiative on pathways to net zero. Sulfate-based H 2 SO 4 and the sulfate-containing nonacid CaSO 4 have effectively suppressed methane emissions in lab studies at a single temperature. The present study analyzes the effect of temperature on the efficacy of these two additives, bridging the gap between common laboratory conditions and average on-farm temperature. We found superior cumulative suppression, higher peak suppression, and longer duration of high-end suppression at lower temperatures when comparing controls to additive experiments at 24, 21, and 18 °C over 120 days. Peak mitigation increased as temperature decreased, culminating at 82.9% and 57.6% for H 2 SO 4 and CaSO 4, respectively, at 18 °C. Additives remained effective for longer at lower temperatures, with H 2 SO 4 maintaining ≥70% peak mitigation (PM) for 102 days at 18 °C, but only 48 days at 24 °C; CaSO 4 retained ≥70% PM for 87 days at 18 °C, but only 38 days at 24 °C. PM for each additive occurs at similar thermal times, despite appearing different at conventional times. Our analysis creates a link between the efficacy of CH 4 mitigation and local temperatures, which can be related to cumulative heat (thermal time/degree-days) to establish site-specific guidance for CH 4 mitigation protocols.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it