Bibliographic record
Abstract
The CVMA president, Dr. Jeanne Lofstedt, represented the CVMA during the annual Ontario Veterinary Medical Association (OVMA) Conference in January, 2003. Dr. Lofstedt, who had been invited to address the participants at the OVMA Annual General Meeting, provided an update on CVMA programs. In particular, Dr. Lofstedt highlighted the national issues, such as the expedited approval of veterinary drugs; species-specific guidelines for prudent use of antimicrobial drugs; and education, licensure and expanding scope of veterinary practice — issues on which CVMA is working on behalf of all Canadian veterinarians. At the CVMA booth in the exhibition area, visitors had the opportunity to receive information on the numerous CVMA initiatives and services, including the public Web site (www.animalhealthcare.ca) and the member Web site (www.canadianveterinarians.net), which provides access to the new CVMA National Benchmarking Program. The CVMA would like to congratulate the OVMA on its successful “Work/Life Balance — Achieving Equilibrium” conference. The CVMA also congratulates Dr. Andrea Chapin, outgoing president, on her successful year at the helm, and wishes Dr. Dave Funston, incoming president, all the best for the upcoming year. (by Jost am Rhyn, Executive Director, CVMA)
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.653 | 0.322 |
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".