What can kinematics tell us about the affective states of animals?
Bibliographic record
Abstract
Abstract An animal's welfare state is intrinsically linked to its affective state. Evidence suggests that sentient, conscious animals can experience a range of affective states, such as pain, fear or boredom as well as positive affects like joy, curiosity, satiation or lust. In the behavioural assessment of animal welfare, there is increasing recognition that it is not simply which behaviours an animal engages in but also the quality of its movement. Kinematics is an approach which is being more widely applied to the behavioural assessment of animal welfare. Kinematics is a field of mechanics that describes the movement of points on a body by defining these points in a coordinate system and precisely tracking how they change in terms of space and time. A major opportunity exists for using kinematic technology to inform our understanding of the emotional state of animals. This review argues that kinematics is a useful methodology for identifying and characterising movement indicative of an animal's affective state. It demonstrates that kinematics: i) appears useful in detecting subtleties in the expression of affective states; ii) could be used in conjunction with, and add extra information to, affective tests (for example, an approach/avoidance paradigm); and iii) could potentially, eventually, be developed into an automated affective state detection system for improving the welfare of animals used in research or production. Furthering our knowledge of animal affective states using kinematics requires engagement from many areas of science outside of animal welfare, such as sports science, computer science, engineering and psychology.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".