Effects of Two Manure Additives on Methane Emissions from Dairy Manure
Bibliographic record
Abstract
Liquid manure is a significant source of methane (CH4), a greenhouse gas. Many livestock farms use manure additives for practical and agronomic purposes, but the effect on CH4 emissions is unknown. To address this gap, two lab studies were conducted, evaluating the CH4 produced from liquid dairy manure with Penergetic-g® (12 mg/L, 42 mg/L, and 420 mg/L) or AgrimestMix® (30.3 mL/L). In the first study, cellulose produced 378 mL CH4/g volatile solids (VS) at 38 °C and there was no significant difference with Penergetic-g® at 12 mg/L or 42 mg/L. At the same temperature, dairy manure produced 254 mL CH4/g VS and was not significantly different from 42 mg/L Penergetic-g®. In the second lab study, the dairy manure control produced 187 mL CH4/g VS at 37 °C and 164 mL CH4/g VS at 20 °C, and there was no significant difference with AgrimestMix (30.3 mL/L) or Penergetic-g® (420 mg/L) at either temperature. Comparisons of manure composition before and after incubation indicated that the additives had no effect on pH or VS, and small and inconsistent effects on other constituents. Overall, neither additive affected CH4 production in the lab. The results suggest that farms using these additives are likely to have normal CH4 emissions from stored manure.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".