Bibliographic record
Abstract
Dr. John Prescott says that in his new role as chair of the Ontario Veterinary College's (OVC's) Department of Pathobiology, he plans to approach the job's challenges as he would a research problem in his bacteriology laboratory. “It's about logic and applied problem- solving,” he says. “First, you have to define the problem, (and determine) what options there are for investigation, then do the study and analyze the results. The biggest difference, the joker in the pack, which makes it so interesting, is that we're dealing with people, and the issues are also multiple and more complex.” Prescott began a 5-year term as chair of Pathobiology in September. Prescott joined the OVC in 1976 as a faculty member on contract in diagnostic bacteriology. He says that one of his goals in his new role is to continue to build the department's strength as a major centre for research and training in pathobiology by helping researchers acquire top-notch laboratory facilities and encouraging highly qualified students, especially veterinarians, to return for graduate study. “The field is changing because of the stunning new technologies,” he says. “I see our niche particularly in graduate-level training. We need to get highly- competent and well-trained people into the system within the different areas encompassed by this broad and diverse department.” The department already has 55 graduate students, and Prescott says it is bursting at the seams. “Funding to encourage graduate student enrolment by veterinarians is a challenge at the department, college, university, and the national level, as the next decade will bring more students in search of higher education, while many faculty members will be retiring.” He is enthusiastic about a recent announcement that the OVC has been approved as a training center for board certification by the American College of Laboratory Animal Medicine — the only certified training centre outside the United States. “This is a critical area that we have to develop nationally,” he says. “The chair's job is to help and encourage these types of initiatives to move forward.” As a personal interest, Prescott also sees opportunities for the department to play a larger role in investigating issues related to destruction of ecosystems, wildlife, and the environment. “I see this as a huge issue facing society,” he says. “We need to look at societal and global issues.” The department currently undertakes work in this area through associations with the Canadian Cooperative Wildlife Health Centre. Prescott will also be busy helping the department to plan for a new pathobiology building on Gordon Street, while continuing to meet other research and teaching commitments. Although his research and teaching responsibilities may be more limited during his 5-year term as chair, Prescott hopes to continue to teach bacteriology to DVM students and to conduct research in bacterial diseases in animals, including Rhodococcus equi pneumonia. (by Karen Gallant, Communications Officer, Ontario Veterinary College, University of Guelph)
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.236 | 0.066 |
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".