Bibliographic record
Abstract
Veterinary medicine has always had a special place in Prince Edward Island (PEI). Veterinarians have traditionally served many small farms and an active standardbred racing industry. They have been a respected and important part of the community. The Atlantic Veterinary College (AVC) began operation in 1986, bringing many veterinarians and a great deal of expertise in veterinary medicine to our small province. Our province can now boast the highest number of veterinarians per acre of all the Canadian provinces, with 12 veterinary clinics to serve our population of 140 000 people. Last year the Veterinary Profession Act was amended to expand the qualifications for membership for those seeking an academic appointment at the AVC to include graduates of schools not accredited by the Council of Education of the American Veterinary Medical Association. Council members of the PEI Veterinary Medical Association (PEIVMA) are directly involved in the production of a newspaper insert to promote excellence in animal care to pet owners. The insert consists of a series of articles prepared by Association members on such topics as dentistry, obesity, nutrition, and vaccination. The insert was produced originally 2 years ago, and then again last year, and was well received by those involved in the pet industry and by pet owners. It will be circulated in a number of Island newspapers and be made available to the PEI Humane Society to use as a handout. The PEIVMA Council is in liaison with the provincial government to draft regulations for the new Companion Animals Protection Act, enacted earlier this year to protect companion animals from abuse and neglect. Under this Act, veterinarians may be required, as experts, to assist in enforcement of the Act. The PEIVMA is in the process of making a dangerous goods course available to its members and their staff in October. It is designed to help the members comply with the requirements for packaging and handling of laboratory samples for diagnosis of infectious disease, alcohol and other flammable products, liquid nitrogen, and biomedical waste. The PEIVMA serves a membership of approximately 110. It continues to function with no permanent office or paid staff. The Association's Council meets monthly, with the annual general meeting in November. It is a pleasure to once again serve on the Council of the PEIVMA. (by Dr. Mel Crane, President, Prince Edward Island Veterinary Medical Association)
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.423 | 0.191 |
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".