Bibliographic record
Abstract
This book is an excellent source to recommend to clients looking for a roadmap to steer them through the complex world of veterinary care. Though numerous books exist for feeding, caring for, or training pets, this book offers something different. Appealing primarily to motivated owners, the material teaches them to become educated partners in their pet’s veterinary care. Topics include finding a great veterinarian, what actually happens during a typical visit to the vet, vaccines, veterinary specialists, alternative medicine, misuse/overuse of steroids, euthanasia, and recourse if therapy or care goes wrong. I found myself looking to see if certain points that I would feel are important, would actually be mentioned, and most often they were. The book paints both veterinarians and the profession in a very positive light. Each area was covered thoroughly and accurately and generally without bias. More controversial topics such as alternative care, core/noncore vaccines, or steroid use, were handled clearly and with neutrality. The primary emphasis was for the owner to become well-educated in that area, so that they might chose treatment that has proven effectiveness through clinical trials, and shown a lack of harm. The chapters were organized logically. The text was clear and easy to read and understand by a lay person. The content was up-to-date and accurate. At the end of each chapter there are checklists and worksheets to assist the owner in putting their new knowledge to use. Even complex issues became much clearer. The author of this book, Dr. Louise Murray, is the director of medicine at the ASPCA’s Bergh Memorial Animal Hospital in New York City. She has been given numerous awards, appeared on American national TV and radio, and in print in USA Today and the New York Times. In reading her work I sense a veterinarian who values quality care for her clients and who wishes to clear the mystery surrounding what we do. On the downside, one must remember that this book is written from an American perspective. This is most apparent in the chapter regarding state board complaints and civil lawsuits. Each state has a veterinary medical board that oversees licensing, complaints, and disciplinary action. In general, this is very similar to the provincial colleges in Canada; however, many Canadian readers would likely miss that point. Web sites also reflect this American bias. Despite this minor regional bias, I would definitely suggest that practicing veterinarians recommend this book to those clients looking for a comprehensive reference.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.181 | 0.156 |
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".