CHECKLIST TO AVOID PUPPY MILL DOGS : Help your clients make the right choice
Bibliographic record
Abstract
In an effort to help fight the battle against puppy mills in Canada, the National Companion Animal Coalition, of which the Canadian Veterinary Medical Association (CVMA) is a founding member, has developed a Checklist for Acquiring a Dog. The checklist provides consumers and prospective dog owners with proper direction and a list of issues to consider when acquiring a dog from a shelter, a breeder, or a pet store. Among other key points, the checklist provides buyers with the information they need to make responsible decisions when acquiring their new dog. Puppy mills have proliferated in Canada during the last decade. Recent efforts in several provinces have led to a number of high profile seizures of puppy mill dogs, but these facilities are still rampant from coast to coast. By helping your clients make informed choices, the CVMA hopes to promote responsible pet ownership and make this a positive experience. The Checklist for Acquiring a Dog is available on the CVMA public Web site at www.animalhealthcare.ca and can be reproduced without permission. The National Companion Animal Coalition is a partnership formed in 1996 to promote socially responsible pet ownership and enhance the health and well-being of companion animals. The Coalition has produced a number of invaluable tools for Canadians, including sample municipal animal control bylaws and a Web site on the subject of children and dog bite prevention (www.dogsandkids.ca). (by Suzanne Lavictoire, CVMA Director, Programs)
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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 | 0.005 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.182 | 0.073 |
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".