The Origins, Founding, and Early Development of the Journal <i>Veterinary Pathology</i>
Bibliographic record
Abstract
The story of Veterinary Pathology begins with an extraordinary veterinarian, Leon Z. Saunders. Intelligent, widely read, and incredibly inquisitive, Saunders was to lead efforts to found the journal. Viewing conversation and criticism as art forms, he was a model for many of us in the give and take of scientific discussion. Born Leon Zlotnick in Winnipeg in 1919, he passed through St. John’s Technical High School and Wesley College to graduate from the Ontario Veterinary College (OVC) in 1943. The story of Veterinary Pathology, however, is more complex because it involves convergence of several events, institutions, and people—the emergence of stellar pathologists in the early 1900s, the postwar economic boom after 1945, growth of veterinary pathology in the 1950s, and a grant from the US government to support the journal in 1964. Thus it is that I preface this story with the status of institutions and people who were to influence the origins of Veterinary Pathology and the forces that transformed aspiring veterinary pathologists.
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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.025 |
| Scholarly communication | 0.024 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".