Crop byproducts supplemented in livestock feeds reduced greenhouse gas emissions
Bibliographic record
Abstract
Crop byproducts can be supplemented in livestock feeds to improve the utilization of resources and reduce greenhouse gas (GHG) emissions. We explored the mitigation potential of GHG emissions by supplementing crop byproducts in feeds based on a typical intensive dairy farm in China. Results showed that GHG emissions associated with production of forage were significantly decreased by 25.60 % when no GHG emissions were allocated to crop byproducts, and enteric methane emission was significantly decreased by 13.46 % on the basis of CO2 eq, g/kg fat and protein corrected milk. The supplementation did not affect lactation performance, rumen microbiota and microbial enzymes at the gene level. Metabolomics analysis revealed changes in amino acid catabolism of rumen fluid, which were probably responsible for more propionate production. In conclusion, supplementing crop byproducts in feeds can be a potential strategy to reduce GHG emissions of livestock.
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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) | 0.000 | 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".