Investigating rumination and eating time as proxies for identifying dairy cows with low methane-emitting potential
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Bibliographic record
Abstract
<h2>Abstract</h2> Identifying cows with low CH<sub>4</sub>-emitting potential can greatly contribute to CH<sub>4</sub> abatement in dairy herds. However, this process has been cumbersome and labor-intensive. Ear tags and collar-based accelerometers measure rumination and chewing behaviors, potentially identifying novel phenotypes in cows. This study aimed to determine whether rumination and eating time are linked to enteric CH<sub>4</sub> emissions and serve as proxies to identify CH<sub>4</sub> yield phenotype in lactating dairy cows. We applied the dynamic time warping model to rumination and eating time datasets to classify cows differing in these phenotypes. We calculated the distances between cows differing in rumination and eating times and depicted them in a principal component analysis plot. From 49 cows in early to mid lactation, 10 low-rumination, and 10 high-rumination cows were selected to test the relationship between rumination and eating time with CH<sub>4</sub> yield phenotype over 7 wk. Enteric CH<sub>4</sub> emissions were measured using the GreenFeed System. The dynamic time warping model identified cows with distinct rumination and eating patterns. High-rumination cows had higher DMI and milk yield, and lower enteric CH<sub>4</sub> emissions than low-rumination cows. High-rumination cows also had lower CH<sub>4</sub> intensity and higher production efficiency than low-rumination cows. Overall, rumination and eating time can be suitable proxies for identifying CH<sub>4</sub> yield phenotype in dairy cows. Further studies, including larger dairy herds, different dietary regimens, and stages of lactation in dairy cattle and other ruminant species to validate rumination and eating time as proxies for identifying CH<sub>4</sub> yield phenotype, are required.
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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 it