Ontario Equine Infectious Disease Report now available to equine practitioners throughout Canada.
Bibliographic record
Abstract
Dear Sir, The Ontario Ministry of Agriculture and Food, the University of Guelph Animal Health Laboratory, and the Ontario Veterinary College have recently established an informal network to improve communication on issues involving equine infectious disease and to streamline disease reporting. One aspect of this collaboration is the publication of a quarterly newsletter, the Ontario Equine Infectious Disease Report. The objectives of this newsletter are to inform veterinarians about emerging issues involving infectious disease and to provide general information on infectious disease. The upcoming edition has information on West Nile virus (WNV) surveillance and Eastern equine encephalomyelitis in Ontario, deworming programs, viral respiratory tract disease surveillance, WNV vaccine safety, intravenous WNV antibody treatment, and mare reproductive loss syndrome. The newsletter is available only in electronic format and is distributed via e-mail to members of the Ontario Association of Equine Practitioners via their list-server. While the newsletter is based on disease issues in Ontario, most of the information would be relevant to veterinarians involved in equine practice throughout Canada. To promote broader circulation, the newsletter will now be made available electronically to any veterinarian or veterinary group. Veterinarians wishing to be included in the electronic mailing should send an e-mail to Scott Weese (ac.hpleugou@eseewsj).
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.055 | 0.020 |
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".