Bibliographic record
Abstract
A client brings in a nine‐year‐old female neutered labrador to ask your advice about a mammary mass. The mass is relatively small and well defined, and you consider it of value to biopsy and/or remove it. However, the dog is 33 kg (ideal weight 15 to 16 kg), struggles to walk and pants after coming from the car park to the waiting room. Looking at previous records, you see that the owner has not visited the practice for several months. At the last visit, they had been to two weight loss clinics and the dog's weight was 27 kg. A note on the record suggests that the owner was reluctant to follow the advice given or to change the dog's food from the working dog diet it was on, and could not afford a prescription diet. When you mention the weight problem and the need for the dog to lose weight before surgery is considered, the client becomes aggressive, tells you ‘the dog is on a diet and the weight problem is under control’ and refuses to discuss attending weight clinics again or changing food. You feel that the welfare of the dog is compromised, and mention this to the client, who becomes angrier and storms out of the practice.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".