Veterinarians, the Royal Society of Canada, and the future of veterinary medicine: Part 1.
Bibliographic record
Abstract
The Royal Society of Canada (RSC) was founded in 1882 with the object of promoting learning and research in the arts and sciences, one means being the election of distinguished scholars as fellows to its membership. Only 4 Canadian veterinary scientists, now deceased, have achieved this honor. They are Seymour Hadwen in 1926 (1), Thomas W.M. Cameron in 1939 (2), Edward A. Watson in 1940 (3), and Charles A. Mitchell in 1945 (4). Presently, the RSC has about 1600 members, none of whom are veterinarians (RSC, personal communication). This questionable showing by our contemporary profession may be another reflection that we are paying insufficient attention to the fact that the profession's long-term strength is rooted in biology and comparative medicine (5). I will try to make this case in the context of describing some of the accomplishments of Cameron and Hadwen (Part I), and Mitchell and Watson (Part 2), who did not make this mistake. Also, the biographies of these individuals are worth attention for their own merit.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.033 | 0.007 |
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".