Training dairy heifers with positive reinforcement: Effects on anticipatory behavior
Bibliographic record
Abstract
Dairy cattle are often restrained for veterinary procedures, but restraint can cause fear responses that can make the procedure challenging for both the animal and the human handler. Positive reinforcement training (PRT) is used in other species to reduce fear responses and there is now evidence that this can also facilitate handling in cattle. The objectives of this study were to test the effect of PRT on anticipatory and play behavior in dairy heifers. We predicted that heifers trained with PRT would show more anticipatory and play behaviors than control heifers in the period before gaining access to a chute. We used 20 heifers (5 ± 0.6 mo old) that had been habituated to the chute area and had previous experience with handling. Heifers were randomly assigned to 2 treatments: control (n = 10) and PRT (n = 10). PRT heifers were subjected to a training protocol that included standard farm handling techniques, as well as target training with food reinforcement. Control heifers were moved to the chute using standard farm handling techniques only. As predicted, PRT heifers performed more behavioral transitions (7.6 ± 0.77 versus 4.4 ± 0.57 transitions for control heifers; F 1,9 = 21.99, P < 0.01), and specifically performed more locomotory play such as jumping (2.1 ± 0.30 vs. 0.4 ± 0.19 jumps; F 1,9 = 57.18, P < 0.01) and running (2.0 ± 0.40 s vs. 0.5 ± 0.16 s; F 1,9 = 20.73, P < 0.01). These results indicate that PRT results in heifers having a more positive emotional state in anticipation of handling, and supports the use of training to improve the welfare of dairy cattle.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".