Prediction of enteric methane emissions from Holstein dairy cows fed various forage sources
Bibliographic record
Abstract
Milk fatty acid (FA) profile has been previously used as a predictor of enteric CH4 output in dairy cows fed diets supplemented with plant oils, which can potentially impact ruminal fermentation. The objective of this study was to investigate the relationships between milk FA and enteric CH4 emissions in lactating dairy cows fed different types of forages in the context of commonly fed diets. A total of 81 observations from three separate 3×3 Latin square design (32-day periods) experiments including a total of 27 lactating cows (96±27 days in milk; mean±SD) were used. Dietary forages were included at 60% of ration dry matter and were as follows: (1) 100% corn silage, (2) 100% alfalfa silage, (3) 100% barley silage, (4) 100% timothy silage, (5) 50:50 mix of corn and alfalfa silages, (6) 50:50 mix of barley and corn silages and (7) 50:50 mix of timothy and alfalfa silages. Enteric CH4 output was measured using respiration chambers during 3 consecutive days. Milk was sampled during the last 7 days of each period and analyzed for components and FA profile. Test variables included dry matter intake (DMI; kg/day), NDF (%), ether extract (%), milk yield (kg/day), milk components (%) and individual milk FA (% of total FA). Candidate multivariate models were obtained using the Least Absolute Shrinkage and Selection Operator and Least-Angle Regression methods based on the Schwarz Bayesian Criterion. Data were then fitted into a random regression using the MIXED procedure including the random effects of cow, period and study. A positive correlation was observed between CH4 and DMI (r=0.59, P0.19). Milk FA profile and DMI can be used to predict CH4 emissions in dairy cows across a wide range of dietary forage sources.
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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 | 0.000 | 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) | 0.000 | 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".