THE SUCCESSFUL PRACTICE OF VETERINARY MEDICINE: NEW: CVMA — MEMBER OF THE NATIONAL COMMISSION ON VETERINARY ECONOMIC ISSUES (NCVEI).
Bibliographic record
Abstract
“The successful practice of veterinary medicine” is one of CVMA's 3 priorities. With its recent partnership agreement with the National Commission on Veterinary Economic Issues (NCVEI), the CVMA will be able to provide new tools to help practice owners to analyze and, where applicable, improve the economic success of their business. Veterinary practice owners, associates, and employees are the beneficiaries of an economically sound business. On July 18, during the 2002 CVMA Summit in Halifax, the CVMA, on behalf of its members, entered officially into a partnership with NCVEI. This new relationship has been negotiated by a CVMA Task Force consisting of Dr. Keith Campbell and Dr. Rob Ashburner. The CVMA joined the founding partners of the NCVEI, the American Veterinary Medical Association (AVMA), the American Animal Hospital Association (AAHA), and the Association of American Veterinary Medical Colleges (AAVMC). Some of the advantages of such an international partnership are the shared access to the significant investments necessary for a benchmarking database and to an interactive system that allows CVMA members to compare their data with USA data and to obtain feedback. The CVMA is the exclusive Canadian member of the NCVEI. The services and benefits resulting from this partnership are offered to all CVMA members.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".