Bibliographic record
Abstract
Abstract Ruminant production systems need to embrace the challenge of reducing greenhouse gas emissions to be in sync with other sectors of society that are adopting net-zero emission goals. The major greenhouse gas from ruminants is enteric methane, which contributes 3% to 5% of total global greenhouse gases. A broad range of potential mitigation strategies has been proposed to decrease methane emissions from ruminants. One promising strategy is the investigational methane inhibitor 3-nitrooxypropanol (3-NOP; DSM Nutritional Products Ltd., Kaiseraugst, Switzerland), which when fed to beef cattle, has decreased methane yield (g methane/kg dry matter intake) by 20% to 80%, depending upon the diet composition and dose. Furthermore, the decrease in methane production persists over several months. 3-NOP reduces methanogenesis in the rumen by inactivating the enzyme methyl-coenzyme M reductase used by archaea. 3-NOP is most effective when incorporated into a total mixed ration. Some advantages of 3-NOP are: only a small dose is required (1–2 g/d); no negative effects on digestibility, animal health or carcass characteristics; rapid degradation to compounds naturally occurring in the rumen (e.g., nitrate, nitrite and 1,3-propanediol); sustained efficacy over time; and risk assessments indicate residues in meat and milk are unlikely. However, 3-NOP is not yet approved for commercial use. Research studies in small pens indicate up to 5% improvement in gain:feed ratio for backgrounding and finishing cattle, although recently completed studies at a commercial feedlot indicate improvements in feed conversion may be less. This presentation will highlight the current findings of beef cattle research using 3-NOP to decrease methane emissions, with emphasis on its potential for decreasing the carbon footprint of beef.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".