Calf- and herd-level factors associated with dairy calf reactivity
Bibliographic record
Abstract
A positive relationship between handlers and animals in farm systems is essential because the human-animal relationship has implications for welfare and productivity. For this reason, on-farm animal welfare assessment protocols often include the behavioral response of animals to humans to measure the quality of the human-animal relationship. The existing literature has described this relationship as being multifactorial in nature. In the current study, we aimed to investigate the potential influence of farm management and infrastructure characteristics, calf manager traits, and intrinsic features of dairy calves on the human-animal relationship. To this end, an escape test was conducted with 698 calves on 30 dairy farms in Chile. This test measured the calf's response to the active approach of an unfamiliar human (and was scored from 0 [fearful] to 4 [friendly]). The explanatory variables used to predict calves' response in the escape test were grouped according to the following categories: (1) farm management and infrastructure (e.g., calf-dam separation age, space allowance); (2) calf manager (e.g., attitudes, behavior, and background); and (3) calf (e.g., breed, sex, age). We concluded that calf managers with additional jobs on the farm, no training, low job satisfaction, a greater proportion of negative contacts, and more negative attitudes were predictive of fearfulness in the escape test. Holstein breed (compared with Holstein and Jersey crossbreeds) was associated with greater odds of fearful calves. Our study confirms the association between animals' fear and handlers' features, which can potentially be used to select employees on a farm. Understanding the factors that influence fear responses in calves may highlight ways to improve the relationship between animals and humans.
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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 | 0.002 |
| 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) | 0.001 | 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".