Rumination and its relationship to feeding and lying behavior in Holstein dairy cows
Bibliographic record
Abstract
The objective of this study was to describe the relationship between rumination and feeding and lying behavior in dairy cows. Rumination time was monitored electronically using HR-Tags (SCR Engineers Ltd., Netanya, Israel). Feeding time and dry matter intake (DMI) were monitored using Insentec feed bins (Insentec BV, Marknesse, the Netherlands). All measures were collected in 2-h periods for 42 mature Holstein cows for a minimum of 9 d in the early dry period. Pearson correlation was used to describe associations, among 2-h periods within cow, first examining the relationship within a single period, and then modeling how this relationship changes when a lag of 2, 4, or 6h was imposed. Periods when cows spent more time ruminating were associated with lower feeding times and lower DMI (r = -0.71 and r = -0.72, respectively), likely because cows were unable to feed and ruminate simultaneously. The correlations with rumination time changed from negative to positive when lags of 2, 4, and 6h were modeled (r = -0.09, 0.24, and 0.15, and r = -0.16, 0.23, and 0.17 for feeding time and DMI at lags of 2, 4, and 6h, respectively). These results indicate that following periods of high feeding times and intakes, cows spent more time ruminating. This relationship peaks at approximately 4h after feeding. Periods of rumination were also associated with time spent lying down. Cows that spent more time ruminating per day, spent less time feeding (r = -0.34) and rumination times did not relate to DMI (r = 0.11). These data indicate that rumination time can be used to estimate within-cow variation in feeding behavior and intake, but daily summaries of rumination behavior are a poor indicator of DMI.
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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 | 0.001 |
| 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 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".