Development and Improvement of an International Webcast Series to Expand the Accessibility of Swine Medicine Resources
Bibliographic record
Abstract
Swine medicine resources and caseloads for teaching and supporting extracurricular training activities vary widely among veterinary colleges and are concentrated in specific regions. Student interest and demand for swine medicine training is broader in geographical distribution. This is illustrated by student membership and attendance at the American Association of Swine Veterinarians (AASV) annual meetings, for example. To explore how concentrated resources might be made more widely available in a cost-effective manner, the Swine Medicine Education Center (SMEC) at Iowa State University's College of Veterinary Medicine looked for ways to leverage existing extracurricular resources with a broader geography of schools and students. This article describes the organization of student chapters of the AASV and the outcomes of a multi-session live audio and video webcast focused on swine medicine topics across North America over a 3-year period. SMEC organized the series with funding provided by the AASV and AASV Foundation. The broadcast series covered a wide range of swine-related topics, including pet pigs, emerging diseases, and regulation of antimicrobials. In its third year, 25 North American and 4 international veterinary schools participated in the series and provided feedback from attendees.
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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.026 | 0.007 |
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".