EXCHANGE OF EXPERTISE AND TRANSFER OF KNOWLEDGE B THE CANADIAN EXAMPLE
Bibliographic record
Abstract
The Canadian experience of international scientific exchanges and technology transfer has been largely established between countries and specific laboratories through contact with individual scientists and veterinarians. Involvement of OIE Reference Laboratories, Collaborating Centres or Canadian National Reference Laboratories has been successful in developing solid working relationships with a number of laboratories worldwide, but challenges remain regarding continuing and maintaining those efforts, and the effective dissemination of the outcomes of those scientific collaborations. The vast majority of technology and scientific transfer arrangements fall outside the current scope of OIE programmes. Engagement of recipients, facilitated through organisations such as the OIE, may enhance the working relationship between the participants. How best to communicate the enormous amounts of knowledge generated from these interactions appropriately? A network approach could be seen as leverage to improve capability and capacity for access and delivery of the best science, provide effective and rapid distribution of information and use of available expertise.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.018 | 0.009 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.002 |
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".