Bibliographic record
Abstract
with rabies surveillance and control.The text will also outline some of the lessons that can be drawn from the Canadian experience that could benefit other jurisdictions.The book began as a discussion between the co-authors/ editors at a rabies conference in Guelph, Ontario, in 2005.They realized that many people, particularly those from the early history of rabies management, had passed away or retired.Further, the corporate memory base was being eroded as important information on rabies was being shredded or stored in various locations without cataloguing.Those trends were accelerating because, as rabies cases declined, government priorities were changing.Resources were being reduced and programs were being off-loaded.We felt that these changing priorities, coupled with the deteriorating collective memory, would make it increasingly difficult for future generations to build on the past contributions to rabies management.Hence, an important goal of the book was to record those contributions and document data relevant to Canada's story.We have done this by selecting authors who are, or have been, involved in rabies management and research in Canada.Further, where possible, the acknowledgments and references in the various chapters cite other involved Canadians.Finally, we have included tables, graphs, and illustrations to provide a statistical and photographic record of the rabies story in Canada.The book is divided into nine parts so that the reader can approach the story of rabies in Canada from a range of perspectives and needs without reading the book from cover to cover.For example, for the reader wanting to appreciate what is currently known about the virus and the
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.547 | 0.397 |
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".