A note on dairy cow behavior when measuring enteric methane emissions with the GreenFeed emission monitoring system in tiestalls
Bibliographic record
Abstract
Changes in the environment or novel procedures can result in altered cow behavior during data collection; training is often recommended to ensure accurate data is being recorded. Currently, little is known regarding the habituation of dairy cows during methane emission testing with the GreenFeed emission monitoring system (C-Lock Inc., Rapid City, SD), or how behavior relates to enteric methane emission measurements. Methane emissions were estimated from a total of 202 Holstein dairy cows (120-150 d in milk) housed in tiestalls as part of a larger project. Cows were tested on d 0 (training day) and d 1-5 (test day) for approximately 10 min, during which behavior was recorded by a trained observer. While cows spent more time with their head outside of the machine on the training day (d 0) than during the test days (d 1-5), the opposite pattern was observed for the number of leg movements. No differences in estimated methane production were found over the different days, though it was negatively correlated with both behaviors. These results highlight the importance of habituation of dairy cows to the GreenFeed system for methane measurements to minimize changes to cow behavior under tiestall conditions, whereas the methane emissions themselves are less affected. However, further research is needed to determine the impact of cow behavior on the reliability and repeatability of methane emission measurements as it may introduce bias in genetic evaluations for methane efficiency.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".