Bibliographic record
Abstract
You are called to a home in the country to examine a dog with blood in its urine. The owners, an elderly couple, have decided to change veterinary clinics. They take you out to the barn where they have approximately 20 dogs and 15 cats in various pens, cages, and box stalls. All of the animals appear healthy and have good hair coats. When you enter the barn, most of the dogs begin to bark, rattle their cages, or jump against the stall doors. You examine the dog with the urinary problem, collect samples for laboratory testing, and provide symptomatic treatment. You learn through your conversation with the owners that they have been “rescuing” dogs and cats for years. Many of the animals they take in are unlikely to be adopted. All are well to over-fed, neutered, and receive veterinary attention when needed. Two collie crosses receive twice daily topical treatments for lick granulomas and 1 cat is tranquilized in efforts to reduce its tendency towards self-mutilation. The noise in the barn is deafening and you are anxious to leave. While driving back to the clinic, you wonder if this couple is doing a good thing or a bad thing. Submitted by Jane McCamus, Kitchener, Ontario Comments: Name: Address:
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 | 0.009 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.023 | 0.018 |
| Insufficient payload (model declined to judge) | 0.032 | 0.019 |
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".