Evaluating undesired scratching in domestic cats: a multifactorial approach to understand risk factors
Bibliographic record
Abstract
Introduction Despite being a natural feline behavior, scratching can become undesirable from a human perspective when directed at household items. This complex behavior can stem from various motivations, ranging from individual cat characteristics to environmental factors. This study investigates the factors influencing the increased level of undesirable scratching behavior in domestic cats, considering both cat-related and environmental aspects. Methods Data from 1,211 cats were collected for this study. An online questionnaire comprising three sections was utilized. The first section gathered caregiver demographics, while the subsequent section examined aspects of cats’ daily routines, social interactions, environments, behaviours, and temperaments. The final section assessed the frequency and intensity of undesirable scratching behavior in cats. Scratching behavior was evaluated based on a combined scratching index. Results The study suggests that the presence of a child may be associated with scratching episodes in the home environment. Additionally, factors such as play duration, playfulness, and nocturnal activity were identified as significant contributors to heightened scratching levels (p ≤ 0.05). Aggressiveness and disruptiveness also played significant roles in increased scratching behavior (p ≤ 0.05). The location of scratching posts emerged as a significant factor, with posts placed in areas frequented by the cat being more effective in redirecting scratching behavior (p ≤ 0.05). Discussion This study reveals several significant associations between cat characteristics, nocturnal activity and play, as well as the environment. It underscores the multifaceted nature of undesirable scratching behavior and emphasizes the importance of comprehensively understanding both the individual characteristics of the cat and its environment to effectively address this behavior.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".