Dairy heifers benefit from the presence of an experienced companion when learning how to graze
Bibliographic record
Abstract
Pasture remains important on many dairy farms, but the age of first contact with pasture varies depending on the month of birth, weaning age, and farm management. Regardless of age, naïve dairy heifers must learn to graze when first introduced to pasture. This study investigated whether being grouped with experienced dairy cows would affect the development of grazing behaviors. Sixty-three Holstein heifers (mean ± SD 14.2 ± 1.3 mo; 546 ± 60.7 kg) and 21 dry Holstein cows (2.6 ± 0.8 lactations; 751 ± 53.9 kg) were assigned into 7 groups of 12 animals (3 dry cows and 9 naïve heifers), and each was divided and assigned to an experienced (3 cows and 3 heifers) and nonexperienced (6 heifers) sub-group. Sub-groups were introduced to pasture in different paddocks without visual contact with any other cattle. No difference was found in the time after introduction to the paddock for heifers to first attempt to nibble grass [experienced: 0:23 (0:17-0:43) vs. nonexperienced 0:40 (0:35-0:46); median (quartile 1 - quartile 3), h:mm]. However, heifers grouped with experienced cows showed a shorter latency to begin grazing [experienced: 0:47 (0:28-00:52) vs. nonexperienced 2:13 (1:25-2:30)]. During the first hour after introduction to pasture, heifers in the experienced treatment showed fewer stomping events [experienced: 2.5 (1.25-4) vs. nonexperienced: 6.5 (4-8)] and vocalized less often [experienced: 3.5 (1.25-5.75) vs. nonexperienced: 7 (5-8.75)]. After this initial period, animals in both subgroups began to graze normally; treatments did not differ in grazing behaviors over the 3-d observation period. These results indicate that grouping heifers with pasture-experienced cows improves grazing behavior of dairy heifers in the first hours following introduction to pasture.
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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) | 0.002 | 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".