Bibliographic record
Abstract
Have you ever had one of those days where nothing goes right? Or a time where you wonder what the heck you’re doing in the sometimes-eventful profession of veterinary medicine? David Perrin does. Frequently. Having read Dr. Perrin’s previous 3 works, I was eagerly anticipating the release of his latest book Never Say Die. I wasn’t disappointed. After graduating from the Western College of Veterinary Medicine in Saskatoon, Dr. David Perrin practiced mixed animal medicine in the Kootenay region of British Columbia for 26 y before publishing his first book of veterinary adventures. If you need a light-hearted book to pick you up at the end of the day or are looking for a fun weekend read, this hilariously illustrated book will serve nicely. Accompanied by his faithful German shepherd Lug and aided by his trusty assistant Doris, Dr. Dave tackles everything from anemic dogs and ornery cows to impacted snakes. There’s something here for everyone. While written in a vernacular that even a layperson can understand, the medicine is descriptive and thorough enough that I found myself pondering diagnoses even as Dr. Perrin describes the case history and clinical findings. The only downside to the book is that it ends too quickly, leaving the reader hoping that Dr. Perrin will continue to publish new installments in his New Adventures of the Country Vet Series.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.080 | 0.052 |
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".