Bibliographic record
Abstract
M. Donald McGavin, William W. Carlton, James F. Zachary, Thomson’s Special Veterinary Pathology (3rd edition), 755 pages, (Mosby), ISBN 0-323-00560-8, reviewed by Jon S. Patterson, DVM, PhD, DACVP Dawn Merton Boothe, Small Animal Clinical Pharmacology and Therapeutics, 806 pages, (Saunders), ISBN 0-7216-4364-7, reviewed by Ron Johnson, DVM, PhD O.M. Radostits, C.C. Gay, D.C. Blood, K.W. Hinchcliff, Veterinary Medicine: A Textbook of the Diseases of Cattle, Sheep, Pigs, Goats and Horses (9th edition), (WB Saunders), ISBN 0-7020-2604-2, reviewed by John House, BVMS, PhD, DipACVIM Yolande Bishop, ed., The Veterinary Formulary (5th edition), 712 pages, (British Veterinary Medical Association/Pharmaceutical Press), ISBN 0-8536-9451-6, reviewed by Richard Vulliet, PhD, DVM D.W. Scott, W.H. Miler, C.E. Griffin, Muller and Kirk’s Small Animal Dermatology (6th edition), (Harcourt Health Sciences), ISBN 0-7216-7618-9, reviewed by Stephen D. White, DVM, Dipl. ACVD Jerrold M. Ward, Joel F. Mahler, Robert R. Maronpot, John P. Sundberg, eds., Richard M. Fredericksen, imaging and graphics ed., Pathology of Genetically Engineered Mice, 406 pages, (Iowa State University Press), ISBN 0-8138-2521, reviewed by Stephen W. Barthold, DVM, PhD, Dipl. ACVP J.P. Morgan, A. Wind, A.P. Davidson, Hereditary Bone and Joint Diseases in the Dog: Osteochondrosis, Hip Dysplasia, Elbow Dysplasia, (Schlütersche Verlag/Iowa State University Press), ISBN 3-8770-6548-1, reviewed by H.A.W. Hazewinkel, DVM, PhD, DipECVS
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.422 | 0.434 |
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".