Bibliographic record
Abstract
A hearty thank you on behalf of CVJ readers and editors to Dr. JoAnn Schuh, who recently resigned as feature editor for the Pathologists' Corner column. The column, which was instigated by JoAnn in 1994, was an occasional feature designed to inform veterinarians of interesting or unusual clinical pathology cases. Through her contacts at the Canadian Association of Veterinary Pathologists, JoAnn solicted and submitted columns to the journal. We are greatly indebted to JoAnn for her strong support of and dedication to the Journal through her hard work on this feature, said Dr. Hare. Dr. Schuh now operates her own veterinary pathobiology consulting business in Bainbridge Island, Washington, USA. Dr. Jeff Caswell at the Ontario Veterinary College takes the reins on this one, and encourages readers, practitioner and academic alike, to submit their clinical pathology cases (see Can Vet J 2002;43:182). (by Deborah Thiel, Interim Manager, Journals)
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 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.131 | 0.189 |
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".