The role of breed versus personality and other demographic factors in predicting chasing behaviours in dogs
Bibliographic record
Abstract
Dogs chase for many reasons, in play, when hunting and potentially to remove a threat. However, assumptions are often made as to why dogs chase particular targets, for example breed or personality are often used as explanations. Little research has investigated chasing behaviour except in relation to predation, therefore we aimed to determine dog-related characteristics predictive for chasing in relation to specific targets, with a particular emphasis on the relative role of breed versus personality, two specific and measurable constructs. An online survey for dog owners about their dog’s chasing habits yielded 903 usable responses. Wildlife, cats and other dogs were the most frequently chased targets and the influence of personality and breed varied with each target. Trait level impulsivity significantly affected the likelihood of a dog chasing targets such as vehicles and cyclists, while components of positive activation (reward sensitivity) affected the likelihood of individuals chasing household appliances and objects in the wind. Considering breed, German Shepherds were more likely to chase cats and things blown in the wind while Border Collies were more likely to chase vehicles and household appliances. Labrador Retrievers were less likely to chase horses, vehicles and joggers. These results shed light on the motivational and emotional basis of chasing, and its heterogeneity in relation to different targets.
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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 | 0.002 |
| 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) | 0.002 | 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".