Personalized Strategy for Animal-Assisted Therapy for Individuals Based on the Emotions Induced by the Images of Different Animal Species and Breeds
Bibliographic record
Abstract
The aim of this study was to apply the FaceReader technique to select the animal species and breed for a personalized AAT based on the emotions (‘neutral’, ‘happy’, ‘sad’, ‘angry’, ‘surprised’, ‘scared’, ‘disgusted’, and ‘contempt’) induced in the persons (18–64 years old) by the images of different animal species and breeds. To implement the aim, the images of different animal species (Canis familiaris, Felis silvestriscatus, Sus scrofa domesticus, Ovis aries, and Equus caballus) and their breeds (dogs: Australian shepherd, pug, Labrador retriever, Doberman, miniature schnauzer, beagle, three mixed-breed types, Yorkshire terrier, Cane Corso, Samoyed, and Chihuahua; cats: British shorthair, Himalayan cat, three mixed breed types, Siamese cat, Sphynx, and Bengal cat; horses: Norwegian Fjord, Exmoor pony, Andalusian, and Friesian; pigs: Vietnamese pot-bellied and Kunekune; sheep: Herdwick sheep and Suffolk sheep) were used. This study showed that the animal species is a significant factor in the intensity of the emotions ‘neutral’ and ‘happy’ as well as valence, and the animal breed is a significant factor for the emotion ‘happy’ intensity and valence. The obtained results could be used as a personalized strategy for improving AAT and helping the individuals to select a pet.
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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 | 0.000 |
| 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) | 0.005 | 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".