Canine household aggression in the caseload of general veterinary practitioners in maritime Canada
Bibliographic record
Abstract
Canine household aggression, or aggression directed toward people living in the same household or familiar to the dog, is a significant cause of human injury and pet relinquishment, and yet there is very little scientific information available concerning the prevalence and characteristics of aggression in dogs in the general veterinary caseload. Because of the complexity of this behaviour, determining risk factors in a manner that will produce clinically useful results requires the use of large sample sizes, appropriate control groups, and adequate details concerning the dog, the aggression, and the home. A two-part study was undertaken to address this problem. The first phase was a cross-sectional survey of dog owners presenting their pets to one of 20 general veterinary practices in maritime Canada in 1996. Single page questionnaires were completed by 3226 owners. This generated the study population for the second phase of the study, a detailed telephone survey of 515 owners. For the detection of risk factors for aggression, dogs were compared on a case-control basis using both univariate and multivariate analytical techniques. (Abstract shortened by UMI.).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".