Enhanced Understanding of Horse–Human Interactions to Optimize Welfare
Bibliographic record
Abstract
) have been domesticated for millennia and are regularly utilized for work, sport, and companionship. Enhanced understanding of human-horse interactions can create avenues to optimize their welfare. This review explores the current research surrounding many aspects of human-horse interactions by first highlighting the horse's sensory capabilities and how they pertain to human interactions. Evidence exists that suggests that horses can read humans in various ways through our body odours, posture, facial expressions, and attentiveness. The literature also suggests that horses are capable of remembering previous experiences when working with humans. The interrelatedness of equine cognition and affective states within the horse's umwelt is then explored. From there, equine personality and the current literature regarding emotional transfer between humans and horses is examined. Even though horses may be capable of recognizing emotional states in humans, there remains a gap in the literature of whether horses are capable of empathizing with human emotion. The objective of this literature review is to explore aspects of the relationship between humans and horses to better understand the horse's umwelt and thereby shed new light on potential positive approaches to enhance equine welfare with 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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".