Symposium review: Scientific assessment of affective states in dairy cattle
Bibliographic record
Abstract
Affective states, which refer to feelings or emotions, are a key component of animal welfare, but these are also difficult to assess. Drawing upon a body of theoretical and applied work, we critically review the scientific literature on the assessment of affective states in animals, drawing examples where possible from research on dairy cattle, and highlighting the strengths and weaknesses of scientific methods used to assess affective states in animals. We adopt the "valence/arousal" framework, describing affect as a 2-dimensional space (with valence referring to whether an experience is positive or negative, and arousal referring to the intensity of the experience). We conclude that spontaneous physiological and behavioral responses typically reflect arousal, whereas learned responses can be valuable when investigating valence. We also conclude that the assessment of affective states can be furthered using mood assessments and that the use of drug treatments with known emotional effects in humans can be helpful in the assessment of specific affective states in animals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".