Greenhouse gas and ammonia emissions from stored manure from beef cattle supplemented 3-nitrooxypropanol and monensin to reduce enteric methane emissions
Bibliographic record
Abstract
Abstract The investigative material 3-nitrooxypropanol (3-NOP) can reduce enteric methane emissions from beef cattle. North American beef cattle are often supplemented the drug monensin to improve feed digestibility. Residual and confounding effects of these additives on manure greenhouse gas (GHG) emissions are unknown. This research tested whether manure carbon and nitrogen, and GHG and ammonia emissions, differed from cattle fed a typical finishing diet and 3-NOP [125–200 mg kg−1 dry matter (DM) feed], or both 3-NOP (125–200 mg kg−1 DM) and monensin (33 mg kg−1 DM) together, compared to a control (no supplements) when manure was stockpiled or composted for 202 days. Consistent with other studies, cumulative GHGs (except nitrous oxide) and ammonia emissions were higher from composted compared to stockpiled manure (all P < 0.01). Dry matter, total carbon and total nitrogen mass balance estimates, and cumulative GHG and ammonia emissions, from stored manure were not affected by 3-NOP or monensin. During the current experiment, supplementing beef cattle with 3-NOP did not significantly affect manure GHG or NH3 emissions during storage under the tested management conditions, suggesting supplementing cattle with 3-NOP does not have residual effects on manure decomposition as estimated using total carbon and nitrogen losses and GHG emissions.
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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 | 0.000 | 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 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".