Behavioral responses to artificial insemination and the effect of positive reinforcement training
Bibliographic record
Abstract
Dairy cattle experience a variety of stressors associated with routine farm practices, including injections, pen movements, regrouping, and artificial insemination (AI). The first objective of this study was to assess the use of ear position and movement parameters in heifers before, during, and after their first exposure to the AI procedure. The second objective was to test whether heifers exposed to positive reinforcement training (PRT) displayed different ear positions and movement parameters during these 3 events. We tested 14 heifers (13 ± 0.7 mo old); 9 were trained using PRT (as part of another study) and 5 had no experience with PRT. Ear positions were recorded using 6 defined categories, and the frequency of each position was compared across periods relative to AI and between treatments. Ear axial and ear forward positions were more frequent before and after AI events than during AI (4.61 ± 0.93 vs. 0.33 ± 0.12 times/event, and 3.17 ± 0.61 vs. 0.42 ± 0.42 times/event, respectively). Another ear position, backward pinned, was observed less frequently before and after events than during AI (0.09 ± 0.06 vs. 3.58 ± 1.05 times/event). We found no effect of PRT on any ear position measured, and no effect of period relative to AI on any of the 4 movements assessed (leaning froward, backward, and steps taken with front legs and back legs). We conclude that heifers subjected to AI for the first time express distinct ear positions and suggest these are associated with negative emotional states. Further work is required to validate these responses and to determine the extent that they can be used to assess affective responses to this and other procedures.
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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 | 0.001 |
| 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".