Inaugural meeting, Canadian Animal Health Laboratorians Network
Bibliographic record
Abstract
On June 4–6, 2002, a group of scientists and managers involved in animal health laboratory work met in Ottawa. They came from laboratories at the 4 veterinary colleges, the provinces, and the Canadian Food Inspection Agency (CFIA) across Canada. They gathered to discuss reports of various diseases; new and continuing trends; and approaches for strengthening relationships to benefit animal health, the livestock industries of Canada, and human health. The meeting was organized by a volunteer steering committee following a conference call last year to which all provincial veterinarians, heads of veterinary college departments doing laboratory diagnostics, and the 4 area laboratory network directors of the CFIA were invited. This steering committee chose to use the name laboratorians (“a person who devotes himself to laboratory work, as distinguished from a clinician” (1)), so as to include veterinary and nonveterinary specialists in all disciplines relevant to animal health laboratory work. Three keynote addresses were given, and abstracts of those presentations are provided below. Dr. Elroy Mann of Health Canada covered perceived benefits of the better linking of surveillance data relating to human health, animal health, and food safety; Dr. Brian Evans, Canada's Chief Veterinary Officer, spoke on emergency preparedness; and Dr. Gary Wobeser, head of the Department of Pathology, Western College of Veterinary Medicine, discussed the often overlooked importance of the wildlife interface in diseases or disease agents that change behavior in some way and become new or emerging problems. A brief management session was held during the course of the meeting to allow discussion of such matters as strategies for veterinary and other animal health laboratorians working together; various approaches to quality assurance; and the training, recruitment, and retention of specialists. Presentations interspersed between the keynote addresses ranged from diagnostic techniques (fluorescence polarization assays), to laboratory data on various diseases (rabies, West Nile encephalitis), to important occurrences (tuberculosis in a dairy herd in Ontario, drug toxicoses), to “we are seeing this new problem, is anyone else?” (hemorrhagic enteritis and splenomegaly in farmed deer). Underlying the scientific discussions lay the main purpose of meeting; namely, to provide a forum for animal health diagnosticians and managers across the geography and various organizations of Canada, where they could make or renew contact after a half-decade hiatus during which there was no national Canadian forum for getting together other than as individual specialty groups. Following this successful inaugural meeting, and with encouragement given by attendees in their responses to a questionnaire and during a postmeeting forum to discuss the strengths and weaknesses of this year's effort, the steering committee has started to plan for next year. Current expectations are that the gathering will be an annual event, alternating between Ottawa and various regional sites, such as veterinary college campuses.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.096 | 0.017 |
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".