US and Canadian cat caregiver’s ratings of cat-cat interactions: A video-based survey
Bibliographic record
Abstract
) were recruited to participate in an online cross-sectional questionnaire to assess: (1) knowledge of inter-cat behaviour; (2) the frequency of positive and negative cat-cat interactions in the home; and (3) factors associated with positive and negative cat-cat interactions in the home. The questionnaire included ten videos (five negatively valenced, five positively valenced), in which participants scored: the overall cat-cat interaction; cat 1's experience; and cat 2's experience, using a Likert scale. Participants were also asked to report how often they see each interaction in their own two cats. Cat behaviour experts (n = 5) were recruited to rate their interpretations of the videos using the same Likert scale as the cat caregiver participants. Overall, our results suggest that overt positive interactions (allo-grooming, co-sleeping) were more likely reported if cat dyads were related or spent more time living together, were neutered males, indoor-only, and/or had a single feeding area. Overt negative interactions (fighting, striking) were more likely reported if dyads were older or had a larger age gap, showed animal-directed aggression, were declawed, and/or had a single litter-box. Participant versus expert ratings of the videos were similar, however caregivers reported certain affiliative behaviours more positively than experts. Caregivers appeared to have a good understanding of their cats' overall relationship, as this aligned with reported cat-cat interactions. These results increase our understanding of the cat-cat relationship in two-cat households, which may be used to inform cat adoption strategies, in-home management, and promote a positive cat-cat relationship.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".